{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "a7f7e185",
   "metadata": {},
   "source": [
    "### Introduction\n",
    "\n",
    "In this notebook we'll take a look at how we can cluster a set of chemical structures using the k-means algorithm. **It's important to note that there is a stochastic component to k-means clusterinh.  The results you see may differ from the published version of this notebook.**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c39a92d",
   "metadata": {},
   "source": [
    "Install the necessary Python libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "5d79bcba",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-05-05T22:03:53.019177Z",
     "start_time": "2025-05-05T22:03:53.017375Z"
    }
   },
   "outputs": [],
   "source": [
    "%%capture\n",
    "import sys\n",
    "IN_COLAB = 'google.colab' in sys.modules\n",
    "if IN_COLAB:\n",
    "    !pip install useful_rdkit_utils pandas scikit-learn tqdm numpy seaborn mols2grid matplotlib"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e041a9a3",
   "metadata": {},
   "source": [
    "Import the necessary Python libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "83352179",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import useful_rdkit_utils as uru\n",
    "from rdkit import Chem\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.metrics import silhouette_score, silhouette_samples\n",
    "from tqdm.auto import tqdm\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "from sklearn.manifold import TSNE\n",
    "import mols2grid\n",
    "import matplotlib.cm as cm"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ca69799",
   "metadata": {},
   "source": [
    "Enable Pandas [progress_apply](https://towardsdatascience.com/progress-bars-in-python-and-pandas-f81954d33bae)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "19fc7d54",
   "metadata": {},
   "outputs": [],
   "source": [
    "tqdm.pandas()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "086464c0",
   "metadata": {},
   "source": [
    "Set up Seaborn plot aesthetics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "8a8b7cde",
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.set()\n",
    "sns.set(rc={'figure.figsize': (10, 10)})\n",
    "sns.set_style('whitegrid')\n",
    "sns.set_context('talk')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c159048",
   "metadata": {},
   "source": [
    "Read the input data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7f9c5add",
   "metadata": {},
   "outputs": [],
   "source": [
    "smiles_url = \"https://raw.githubusercontent.com/PatWalters/practical_cheminformatics_tutorials/main/data/cluster_test.smi\"\n",
    "df = pd.read_csv(smiles_url,sep=\" \",names=[\"SMILES\",\"Name\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff6af938",
   "metadata": {},
   "source": [
    "Add molecular fingerprints to the dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "1a884976",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "bf9efb0d63ca4660a3770c6c32ae600f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/735 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "smi2fp = uru.Smi2Fp()\n",
    "df['fp'] = df.SMILES.progress_apply(smi2fp.get_np)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3010d3fb",
   "metadata": {},
   "source": [
    "Convert the fingerprints to an X matrix to make sklearn happy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9d011009",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = np.stack(df.fp)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ab523f9",
   "metadata": {},
   "source": [
    "Clustering with k-means only requires a few lines of code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "014c5c1d",
   "metadata": {},
   "outputs": [],
   "source": [
    "num_clusters = 8\n",
    "km = KMeans(n_clusters=num_clusters,random_state=42,n_init='auto')\n",
    "km.fit(X)\n",
    "cluster_list = km.predict(X)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eaf37142",
   "metadata": {},
   "source": [
    "Plot the cluster populations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "dff2820e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = pd.Series(cluster_list).value_counts().sort_index().plot(kind=\"bar\")\n",
    "ax.set_xlabel(\"Cluster Number\")\n",
    "ax.set_ylabel(\"Cluster Size\")\n",
    "ax.tick_params(axis='x', rotation=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8fb9157a",
   "metadata": {},
   "source": [
    "One potential drawback of k-means clustering is that it requires you to specify the number of clusters.  One means of determining the optimal number of clusters is by maximizing the silhouette score. \n",
    "\n",
    "Silhouette Score =  $\\frac{(a-b)}{max(a,b)}$ where **a** is the average intracluster distance and **b** is the average intercluster distance.  The Silhouette Score ranges between -1 and 1 with a vaule closer to 1 representing a good match between a point and other members of the same cluster.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "7fbd73fc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "671853523b6a4fa6948c503a49b867ed",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/20 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cluster_range = range(5,25)\n",
    "score_list = []\n",
    "for k in tqdm(cluster_range):\n",
    "    km = KMeans(n_clusters=k,random_state=42,n_init='auto')\n",
    "    cluster_labels = km.fit_predict(X)\n",
    "    score = silhouette_score(X,cluster_labels)\n",
    "    score_list.append([k,score])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "605e1e75",
   "metadata": {},
   "source": [
    "Put the silhouette scores into a dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f2a1b2e8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>K</th>\n",
       "      <th>Silhouette Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5</td>\n",
       "      <td>0.256244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6</td>\n",
       "      <td>0.202253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7</td>\n",
       "      <td>0.189455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8</td>\n",
       "      <td>0.272224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9</td>\n",
       "      <td>0.222486</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   K  Silhouette Score\n",
       "0  5          0.256244\n",
       "1  6          0.202253\n",
       "2  7          0.189455\n",
       "3  8          0.272224\n",
       "4  9          0.222486"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score_df = pd.DataFrame(score_list,columns=[\"K\",\"Silhouette Score\"])\n",
    "score_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed5e7724",
   "metadata": {},
   "source": [
    "Plot the silhouette scores as a function of the number of clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "5580a38b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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uWMgdrYamXDBc7vjHXKmrE9lzsFTem7tFLjy5v7gNQR0AAABgcet35Mt/Plonq7ccCnh5VJTItFFd5dLTBkrHnBRxm77dMuWMiT3l4wXbtftvfL5Rpo7sKu2zk8VNCOoAAAAAi9q+t1he/WSdLPx+31GvmzCko1x++nHSo1O6uNkVZxwn36zK0xarqxUOz72/Wn539XhxE4I6AAAAwGL2HirTRvXPXb5bKy00GtY3V5v+OKBHtlnHs5TU5Hi5+qzB8tgby7X7363ZJ0vW7Zcxx3UQtyCoAwAAACzicFGF/O/zjdoONjXd0qh/90y58oxBrp3w2JyTxnTTPmdrt+Vr9595d5UM7XuSJMS5Y2gKQR0AAABgsuKyapkxZ5N8OH+rVHtqA17XrUOaVmKoyi2jVBMdjhIVFSU/mz5cfv7oV1JbWyf7Dpdrn0/Va+gGBHUAAACASSqqPPLB11vkna82S3mlJ+B1atjHZacNlKmjujpmaXgk9eyULmcf31ve/3qLdv/tOZvkxNHdpFOu8wfIENQBAAAAbUwN9Pj02+3y5hcbtQEfRllpCdpqArWHLS422rQz2tGlpw2QeSt2S35xldR4arUyzHuvneD4DCdBHQAAANBGvN5ambNkl7w+a4McKqwIeF1KUpxMP7Gvlm1KTOBpeiiSE+PkmnOGyF9fXardX7r+gDY4ZeLQTuJkfLcAAAAAEab6vBaszpNXP1mvLck2SoiPkXOm9JYLTuwnqUlxpp3RKaaM6CKffbdDVm2u3+mnVhyM7N/O0YGyc/9lAAAAgMnq6upk2YYD8son62TL7qKA18XGRMnpE3vKRaf0l6y0RNPO6DRRUVFy4wXD5NZHvhSPt04OFlRoZa5XnjlInIqgDgAAAIiA9dvz5ZVPN8j3Ww8HvFzNPDlxTDe55NSB0iE72bTzOVm3Dmly3tS+2rAU5d2vNmtDU9TLnYigDgAAAAijfQXV8sXKYtmUt/uo100a1kmbaNm9Y7opZ3OTH5/SX1verjJ1KmOnhqb88YZJjhyawjgdAAAAIEw27CiQZz87IJvyKgNePqJ/O3n0thPkzqvGEdC1kcSEWLnu3CH6/ZWbDsn8FXniRGTqAAAAgDBZsm6/1Bp2hw/okSVXnnmcDOvbzsxjudaEIZ1k9MD22hRM5fkP1sjo49prUzKdhEwdAAAAECaFpVX67R+M6y5/vWUKAZ2JoqKi5Ibzh+n7/vKLK+W/szaI0xDUAQAAAGFSWOJfJN6tfaoj+7fsplNuivzopH76/Q/mbZXte4vFSQjqAAAAgDApKvNn6jJS4009C/ymn9RPOuYk6zsDn35nlbZuwikI6gAAAIAwKSzxB3WZaQmmngV+CXExWhmmj1oz8eXSXeIUBHUAAABAGKjMj7GnLiOVoM5KxhzXQSYM6ajff3HmWimtqBEnIKgDAAAAwqCy2ivVNf7Rl5kEdZZz3blDJT4uRrutAvBXP1knTkBQBwAAAIS59FJJS6GnzmraZydrS8l9PlmwTTbvLhS7I6gDAAAAwqDIUHqZnBAtMdFMvrSi86f1kS7tUrTbtXUiT89YpQ1PsTOCOgAAACAMCgyZutREnmZbVVxsjNx4gX9oyoadBfL5oh1iZ3y3AQAAAGFgHJKSkljftwVrGtG/vRw/vLN+/+WP1kpxmX/HoN0Q1AEAAABhLr9MIVNnedeeO0SSEuqD75LyGnnFxkNT+G4DHEZdZfrfvMPyxteHAq4YAgCAthuUkkqmzvJyMpLkklMH6vc/X7RLdh2y53MngjrAYWYt3CnrdlXI+t2V8vE3280+DgAALi2/5Gm2HZw9pbd075im3/9ocaEth6bw3QY4zL7DZfrt3QdKTT0LAABuzdTRU2cPsTHR8jPD0JR9BTWyeJP/uZRdENQBDlNWWaPfPlxUYepZAABwb/klT7PtYkifXDlxdFf9/pxVRUftHLQ6vtsAhymr8Oi3DxdXmnoWAADcOyiFTJ2dXH32YElOjNVuV9XUyX9nbRA7IagDHKa0wp+pKyiusmVdOAAAdlPjqQ34HUxPnb1kpSXKZacN0O9vzSsSO+G7DXCYMsMvFG9tXcBVQwAAEBnFZYG/b8nU2c/pE3vKuP4p0iEzTi7+gT/As4P6HCMAxyg39NQph4oqJCs90bTzAADgBgWGHqyEuCiJi4ky9TxouZjoKDlzTJZ2e8TA9mInZOoAB1GllsZMnXK4iL46AAAijcmXMBNBHeAgldUeadhCR1AHAEBbD0nhKTbaFt9xgIOUlgdm6RTWGgAA0NbrDMjUoW0R1AEO3VHnQ6YOAIDIKzRm6hJ4io22xXcc4CDGUco+ZOoAAGjjoI7yS7QxvuMAx5dfkqkDACDSGJQCMxHUAQ7ScPKlQlAHAEDbDkpJJVOHNsZ3HODwnrqKKs9Ru+sAAEB4kamDmQjqAIeXXypk6wAAiOye2KKyav0+PXVoa3zHAQ7P1CkMSwEAIHJKyqu1wM6HlQZoawR1gMN76hQydQAAtM3ky9iYaEmIizL1PHAfgjrAQSi/BADA3CEpmWnxEhVFUIe2RVAHOIix/DLRcJXwEOWXAAC0yZCUzNQEU88CdyKoAxyktNzfpN0uM06/nU+mDgCANim/zCCogwkI6gCH9tS1z/AHdQxKAQAgcsjUwWwEdYBDyy8DgzoydQAAtEVQl5Eab+pZ4E4EdYBDeLy1UlHl1e+3z4wNKAtRrwcAAOFXVOpvfyBTBzMQ1AEOXWfQzpCpq6sTyS8mWwcAQCQUlvp/x2akEdSh7RHUAQ4svVSTlJMToiUtmWEpAABEWmFApo7yS7Q9gjrAgTvq1NLT6KgoyclI1F9GXx0AAOFXV1fXoKeOTB3aHkEd4MDyy6T4+v/aORlJ+svYVQcAQPhVVnulusbf005PHcxAUAc4cvF4/X/t7HQydQAARJIxSxcdJZKWQvkl2h5BHeDA8stEPVNnDOrI1AEAEMmgLj0lQWJUZAe0MYI6wIHll3pQR6YOAICIUmuDfNhRB7MQ1AEOURoQ1NVfJczJJFMHAEBbBXWZrDOASQjqACdm6proqVMTugAAQPgUBWTqCOpgDoI6wNHTL/1BXY2nVkoMfXcAACC8PXVk6mAWgjrAkeWX9f+1U5PiJD7W/9+cEkwAACIY1JGpg0kI6gAHD0qJ0haQ+3fVMSwFAIAI9tQR1MEkBHWAEzN1cf5xygxLAQAgcii/hBUQ1AEOztQpOelk6gAAiBQGpcAKCOoAB1BTLUsbGZRy9AJygjoAAMJFDSEz/v4lUwezENQBDlBV4xWPt7bxTF1AUEf5JQAAkcjSKfTUwSwEdYDDSi+Ne+qUnEzKLwEAiPSQlOTEWImPizH1PHAvgjrAYUFdXGy0xMUaBqWQqQMAICJYZwCrIKgDHMBYz5+SGBfwOuOgFLV8XJVqAgCA1mNICqyCoA5wWKYuJSk24HVZ6QkS5U/cST4lmAAAhAXrDGAVBHWAw4K61KTATF1sTLRkGX7RHKIEEwCAsGDxOKyCoA5wWvllg6BOyc5gWAoAABEN6sjUwUQEdYDjyi+PDupy0v3DUvLJ1AEAEBaUX8IqCOoAF2TqWEAOAED4MSgFVkFQBzgtU9dg+qWSa9hVR08dAADhwUoDWAVBHeCwTF3DQSkKmToAAMKrtrZOisqq9fuUX8JMBHWAw1caNNxVR1AHAEDrlZRXa4GdD5k6mImgDnDF9Et/pq6guDLglxAAAGjd5Mu42GhJTjz6oirQVgjqAIfvqWtYfulV5SKGX0QAAKD1Q1KioqJMPQ/cjaAOcFqmrpFBKcmJcQFXEBmWAgBA67DOAFZCUAfYnCqlLK80BHXJRwd1CsNSAAAIHyZfwkoI6gCbq6jySJ2hRa6xTJ3CsBQAACLTU0dQB7MR1AE2Zyy9VFKaaNQ2Dks5TPklAACtQvklrISgDnDQkJSkhFiJiWn8v7VxATmZOgAAWqeotDpgUApgJoI6wFE76hovvTy6p45MHQAArVFY6r9ASqYOZiOoA2yutKK62XUGPjnpDEoBACAyg1LiTT0LQFAHuCZTR/klAADhUFdXJ4WG8svMNP+FU8AMBHWAgwalNJupy0wMmJhpXIMAAACCp36PVtd49ftMv4TZCOoAJy0ebyaoy0hJkNiYKP0+2ToAAFo/JCU6SiQthfJLmIugDnBQ+WVzmbro6CjJCuirY1gKAACt7adLT0mQGBXZASYiqANckqlTGJYCAEB4J19mMCQFFkBQB7gkU9dwWMohMnUAAIQkcEgK/XQwH0Ed4JLplw2HpZCpAwAgHOsMmHwJ8xHUAa4qv/Rn6vIJ6gAACElRqT+oy0ij/BLmI6gDXFV+yaAUAADCm6mj/BLmI6gD3JSpCwjqyNQBABCKQkOmjqAOVkBQB9hYjadWqqq9QQd1uZlJAb+QPN7aiJ4PAADHZ+oYlAILIKgDbKy80p+lC6b8Mtuw0qCuTiS/mGwdAACtytQR1MECCOoAh5Reqr2nSQmxzb59fFyMpCX7G7oZlgIAQMurZIz97BmUX8ICCOoAB60ziIqKOub70FcHAEB4Jl8q9NTBCgjqAIdk6lKTghupbOyrYwE5AACh99MlJ8ZqVTCA2QjqABsrKzdm6povvfQhUwcAQOiYfAkrIqgDbKy0suWZuhzDsBR21QEAEHqmjn46WAVBHeCgnrpgZGf4yy/J1AEAEHpPHZMvYRUEdYCNlZZXtzioy80kUwcAQKhYZwArCq4JxwK2bdsmTz75pCxdulQOHz4sHTt2lDPOOEOuv/56SUlJadHH+uqrr+TVV1+VNWvWSGlpqWRkZMjo0aPl2muvlWHDhjX6Phs2bJBnn31WFi5cKIWFhZKZmSmTJ0+Wm2++Wbp16xamfyXQMmWVnqB31PnkNMjU1dXVBTU1EwAA0FMHa7JFpm7VqlVywQUXyMyZM6Vdu3Yybdo0KS8vl6efflouvvhiKSkpCfpjPfroo3LDDTfI/PnzpUuXLjJ16lRJT0+Xzz77TC655BJ57733jnqfTz75RKZPny4ffvihtG/fXnufuLg47W1/9KMfya5du8L8LwYil6kzDkpRu3ZKDMNWAABA8D11ZOpgFZYP6mpqauS2227TgriHHnpI3nzzTXn88cdl9uzZctJJJ8nGjRvlkUceCepjLVmyRJ555hlJTk7WMnUzZszQsn8qaPvDH/4gHo9H7rnnHtm3b5/+Prt375a77rpLu/33v/9d3nnnHe19Zs2aJRdeeKGWtbvvvvsi9u8Hwt1TpzJ68bH+//qUYAIAEDwGpcCKLB/UffTRR7Jnzx6t1PH888/XX56YmCgPPPCAFqC9/fbbUlxcfMyPpd5OUWWWY8aMCXidyvipDFxVVZWWtfP597//rQWU1113nZx55pn6y1Wm7re//a1WBqqCQPV+QFsrC5h+GVxQp0otG5ZgAgCAEAalENTBIiwf1H355Zfa36eeeupRr8vKypLx48dr2TxVTnksKhDs37+/9j6N6d27t/b3gQMH9JepLF5sbKxcffXVR719amqqzJ07Vws8ExL4T422Vxqwpy64oE7JYVgKAAAtVltbJ0Vl/taHLMovYRGWH5SiyiuVAQMGNPr6fv36aYGfGmRizKQ15lhlkitXrtT+7tSpk156WVBQoAWCqu9ux44dWpCneujUcBVV/tkw4xcuaniF1+sVsxgf28xzGHGm5ssvkxNitDMEc6Zswy+hgwXlET+72Z+nxnCm4HAme5+LMwWHMwWHM4kUl1VrgZ1PalLsUY/L5yk4nCn4mMARQd3+/fu1vzt06NDo69XglIbZtVDMmTNHli1bppVVnnLKKdrLVBDne+znn39e66lTfXfG0szzzjtP/vSnP2nvF04VFRWyYsUKsYLVq1eL1XCm+v/kJRX+q4U7t2+WioK4oM7kqfKXK2/alicrVrRdto6vXXA4k33PZNVzcabgcKbguPVMBwr9F1NjokU2rl/T7ARpt36eWoozuaD8UgU3vtLJxvhervreQqWyfHfeeafeb6f65BTfVE2Vwfvb3/6mTcdUA1IWL16sDWvJzc3VJmAGO6gFCKcab53U1vrvJ8YH/985PSlGv11cbo0rUQAAWF1ppf93ZkpiDCuBYBmWz9TFxMRIrfGZaytTk42tS1C77tQUyxNPPFFuueUW/XW+4SdqCMull14qd999t/660047TcvgqQErapKmCgZVkBcuSUlJTZactgWVcvZdoRg6dKj2dTAbZwpU3wuXp98fP2aExMfFBHWm8pi98umypdrtmro4GTFiRETPyteOMzn9TFY9F2fiTJwpvEpW7BGRQ9rt9tmpjf7+5PPEmcJJJZ98SS5bB3VqsbgKuJqaLllZWT+5T03BbKlPP/1Um2CpPlFqEIvKuBm/eMaPedVVVx31/uo/8uDBg7Ul5mpdwumnny7hoq78WOEbSVHnsMpZfDiTSEW1/2KHWlGQlBgf9JnaZfm/t/OLK9v03HztgsOZ7Hsmq56LMwWHMwXHrWcqLvOXX2amJR7z8dz6eWopztS0YLPBli+/VMu+lYMHDzb6el8vne/tgqV2zan9dyqgu/zyy+Uf//iHxMcHPinOzs7Wb3ft2rXRj+N7eX5+foseHzBr8qWSk+5faaCWj1fVUIIJAMCxFLLOABZl+aDOV4K4adOmRl+/efPmgLc7FlXKqbJzqicuOjpafve738nvf/977XZjj+2Ljn0DWxo6dKg+BZ+TkxPkvwiIwI665JYFdVnpCWK88JPPrjoAAFq0eDyTdQawEMsHddOmTdP+VgNKGlLrBhYuXKjtiJs4cWJQH0/1xb377rtaz5rK1l155ZVNvq3aQ+dbWfDBBx8c9XqVPVy7dq22x2706NEt+FcBYc7UJbYsqIuNiQ7YrXOIXXUAALQoU5dBpg4WYvmgTq0X6NKli3z11VfyxhtvBPTSqSybmnp50UUXBZRKqmXkW7Zs0f6o2z5qUuWMGTO0+tinnnpKG4xyLNddd5329zPPPBOw4Ly0tFTuuusu7fHPOuussA5JAVq6o66l5ZdKdoa/BPMwmToAAI6pyFh+SaYOFmL5QSlqZcHDDz+sTZe899575c0339T62JYvX6710w0ZMkRuv/32gPdRpZK+ReRffPGF9vZqms1jjz2mvUwFYCq4U38aM2XKFDn33HO121OnTtUmYv7zn/+Ua665RoYPH64FkGrNgeqjGzhwoBbcAW0toPwy6eghKceSk54om/XySzJ1AAC0pPwyi0wdLMTyQZ0yduxYeeutt+SJJ56QRYsWaX10KlBTGbqrr75am5AZzDjQvXv36kHfzJkzm3zbrKwsPahTbr75Zq288qWXXtIWgq9fv17LHl522WXy05/+NKTJm0B4B6W0/L9yToZ/9yOZOgAAjr0+q7C0Wr+fQaYOFmKLoE7p37+/NtwkGCrgU0Gc0aBBg456WUuonr1g+/aAti6/TE0OIVNnKL+kpw4AgOZVVHmk2jAtmumXsBLL99QBOHb5ZUsHpSi5mWTqAAAIZUhKdJRIWkrLL6gCkUJQB7hwT13DXXUEdQAANK+oxF96mZ6SIDEqsgMsgqAOcET5ZSjTL/2ZuoLiSvHW1oXtbAAAOE1hqf8CKJMvYTUEdYBNlRqnX4ZQfmkclKICOuOYZgAAEChgSEoqpZewFoI6wKbKyqtbVX6ZnBgnyYn+WUmHGZYCAEBQ6wwyU/0XRgErIKgDbKi2tk7KqzytKr9UWGsAAEBwCkv8vycz0sjUwVoI6gAbKq+skTpDC1womTqFYSkAAASnyFB+yToDWA1BHWBDpYYhKb5SylAYh6VQfgkAQHArDbIYlAKLIagDbD75UvXFhTpWOTeTTB0AAC3tqcsgUweLIagDbJ6pC7X08uieOjJ1AAAEk6ljpQGshqAOsPuOutYEdekMSgEA4FhqPN6A371k6mA1BHWADZWFLVNH+SUAAC0ZkqIwKAVWQ1AH2L38MsQhKQ3LLyuqPNpUTQAA0HQ/neplj4+LMfU8QEMEdYDdyy9D3FHnKx8xDlkhWwcAwDH66cjSwYII6gAXD0qJjo5irQEAAC3I1DEkBVZEUIdGrd9RIM9+ul/e+y5fvLWGLdewXqauFeWXCsNSAAAIPlPHkBRYEUEdGvXm7I2Sl18jK7aWy6pNB80+DprL1LWi/LLhsJRDZOoAADhKEesMYHEEdWiUMTmXd6jMzKMggisNlJxMMnUAAARdfkmmDhZEUIdGZRmuQhUYfpDBWdMvlZx0f6Yun6AOAICjsHgcVkdQh0ZlpxuCumKCOmtPv4xv1ccyrjVgUAoAAEcjUwerI6hDo7LS/E/0C0rI3jh1+mXDoO4QmToAAI7CoBRYHUEdGpVlyNTlk6mzlBqPV6prvGHrqcvNTApoBPd4a1v18QAAcJLa2jopNgR1xhYVwCoI6tCobMOYezJ11lJW4Qm439pMnfFrXVengni+3gAA+JSUVwcMkCNTBysiqEOjjFehisuqxUv2xjJKK6oDlocnxse06uPFx8VImqEvj2EpAAA0XnoZFxstyYmxpp4HaAxBHRqV1SB7Y/yBBmutM4iKimr1xwwclkJQBwBAo0NS0hLC8nsXCDeCOjQqIS5GEuL8P7QoyXPmkJTG+upYQA4AQONBHaWXsCqCOjQpLclf1sdaA2tm6sIV1JGpAwCgcWqImA/rDGBVBHVoUqohqDtMps6y5ZfhkGMot2VXHQAATSweJ6iDRRHUoUlpif5vjwKCOkeXX2Zn+MsvydQBANB0Tx1gRQR1CCpTR0+dwzN1AeWXZOoAAGg0U0dQB4siqEOT6KmzfqYuNQKDUlSmrk6NPAUAAAE9dQxKgVUR1CG4TB0LyB1dfmnM1NV4aqWk3P8YAAC4mbH8MougDhZFUIcmpSXRU+eW8kv1ceJj/V9vSjABAFC7eusCVxpQfgmLIqhDUOWX6gdabS0leU5daaAWqeYwLAUAgAAVVR6p9tTq95l+CasiqENQ5Zfe2jopLqs29TyIXPmlkpPJsBQAAJoakhIdJZKWEm/qeYCmENShSQmxURIXE6XfL6CvzrHll0pOOpk6AACMikr8F7TTUxIkRkV2gAUR1KHZkrxUQ18daw2sUdsfsUxdwFoDvtYAABSW+n8fss4AVkZQhxasNeCJvtkqq70BvY2RCuoOUX4JAEDgkJRUSi9hXQR1CLqv7jBBnaVKL8NefmnYVZdPpg4AACks9ZdfZqb6L34CVkNQh2alJbKA3EqMpZfxcTESF+v/+oS3/JJMHQAAhYZ5AhlpZOpgXQR1aBY9de4YktJwUIpaPl5V4w3rxwcAwG6KAjJ19NTBugjq0Cx66py/o84nKz1BogxDvSjBBAC4nXGlQRaDUmBhBHUIuqcu39AsDHOUVlRHLFMXGxMdcBWSYSkAALcLKL8kUwcLI6hDizJ1aqQ+zBOpdQaNDUthrQEAwO0CBqWQqYOFEdShWWmGnroaT+1R0xfRtsrKI9dTp+Sk+4el5JOpAwC4WI3HG/C8h0wdrIygDs1Kio/WyvJ8GJZirtLKCGfqWEAOAMBRQ1IUBqXAygjq0KyoqKiAxmDWGjh3+qWSk+Evv6SnDgDgZsbF4ymJsdoqIcCqCOoQ1FREn3xDwzDaXml5ZDN1uZlk6gAAaDj5ktJLWB1BHY4pMFPHE30zlUW6/NKwq46gDgDgZsZMHUNSYHUEdTimLMPwjMMEdY4uv8w29NSpAN5by7RTAIA7kamDnRDU4ZjoqXPRSgNDUKcCuiLDLzQAANzE+DuQTB2sjqAOx5SdZhhzT6bO0Zm65MQ4SU6M1e8fZlgKAMCljOWXWWTqYHEEdWjRoBR66syjMmfllZ6IZuoU1hoAABAY1GWQqYPFEdThmLIMmboCpl+aptwwJCVSmTqFYSkAAAT21LGjDlZHUIcWZeoqqrxSUeXPFsGc0suoqPpSyUgwDkuh/BIA4FYMSoGdENThmNQPsugo/31KMM3fUZecECvRxi9KGOVmkqkDALhbbW2dFBuCOuPQOMCKCOpwTDHRUQFTnxiWYn6mLlL9dEf31JGpAwC4T0l5tRi3+jD9ElZHUIcW76pjrYE5Sg09dalJ8RF7nBzjXkIydQAAlw9JiYuNlqQE/2RowIoI6tDiYSn5DEsxvfwyspk6yi8BAO4WMCQlLUGiVDM7YGEEdQhKdkCmjif6pu+oS26b8ks1FKfh1E0AAFy1zoAhKbABgjq0eAImPXXmKDMEVykRmnzp++Wl+ih9yNYBANymiHUGsBmCOrQ4U0dQZ47S8uo2Kb9UUzVZawAAcDNj+SWTL2EHBHVoeU8dg1JMUVbhaZPyS4VhKQAAN6P8EnZDUIegZBvKL+mpc3b5ZcNhKYfI1AEAXD4oBbA6gjq0eKVBaUWNVNd4TT2PG7VV+aWSk0mmDgDgXmTqYDcEdWhx+aVSYPhhh7bP1EW+/NKfqcsnqAMAuHhQShZBHWyAoA5BUYs301P8C68pwTR5T13Eyy8ZlAIAcKe6urqATB3ll7ADgjoEjQmYFtpTl9R2Qd0hMnUAABdRO1qrPbX6fcovYQcEdQiacaQvmbq2pXoYjb9gIl1+mZuZFFCC4vH6HxsAALcMSVFrW9MMlUqAVRHUIaRhKfn01JmWpWuL8ktjVraujswsAMA9jKWX6SkJEqMiO8DiCOoQ0hN9MnVtS00c9YmNiZKE+JiIPl58XIykJfuvTDIsBQDgxiEp9NPBLgjqELQsw646Mjcm7qhLipOoqMhfNQzsq2NYCgDAjesMKL2EPRDUIWgMSnHH5MvGJ2Dy9QYAuEPA5MvUwJVOgFUR1CGkXXUFxfTUmTb5MsJDUhoblkJQBwBw46AUyi9hFwR1CClTV1RWJV4mIprSU9dmmTrD15tddQAANwZ1lF/CLgjqEFJPnZqIaPyhh7bL1KmeuraQnUGmDgDgPkWl1Y2ucwKsjKAOQUuMj5XkxFj9Pn11ZpVfxpvQU0emDgDgDoUl/uc3LB6HXRDUoUXoq7NC+aU/sG7Lnro6lZ4FAMDhCg2ZOnrqYBcEdQg5e0Omztnll8avdY2nVkoMEzgBAHCiGo834Hcu0y9hFwR1aEWmjqDOyeWXqUlxEh/r/xFBCSYAwOkKS/xZOoVBKbALgjqEvoDcsMcFkVVa4f8lk9pG0y/VgvMchqUAAFykyDAETrU7xMfFmHoeIFgEdQh5rQGZurZTVuHRb6e00Z46JSeTYSkAALeuM6CfDvZBUIcWyTIEdfTUmTMoRZVFtpWcdDJ1AAD3KDRUITEkBXZCUIcWyTaWXxLUtQk1dbKssu0HpRy91oCvNwDAPZk6gjrYCUEdQh6Uoq5m1dYy5j7SKqo8AZ/nVJOCukOUXwIAXJSpo/wSdkJQh5B76ry1dVJcFjglCpEtvVSS22hQipJj2FWXT6YOAOCiQSlZBHWwEYI6tEhyg0lQBSU80W/LdQYJ8TESZ1gz0Lbll2TqAAAuytRRfgkbIahDi8fc01dn4o66Niy9bDgoRS0fr6rxtunjAwBgWk8dmTrYCEEdWowF5OaVX7blkBTfXsKoKP99snUAACdjUArsiqAOLZZtKMnLL2YBeVtm6lLasJ9OiY2JDrhSyQRMAIBTabMCyNTBpgjq0GIsIDex/LINF483NiyFoA4A4FSl5dViHOpNpg52QlCHFssy/JDLZ1CKo8svlRzjwnnKLwEALhiSooaSJSXEmnoeoCUI6tDKTB3ll04elKKwgBwA4MZ+OjUcDrALgjq0WJYxc0P5pfMzdRn+8ksWkAMA3JCpo58OdkNQh1Zl6lRQV1dnKECH4zJ1uZlk6gAA7srUZRDUwWYI6tCqnroaT21A0IHIZupMKb807KojqAMAOFWRIagzPtcB7ICgDi2WnhIvsTH+OnNKMNtwpYEJQZ1xhYWadqpGPgMA4OTySzJ1sBuCOrSYahzODFhAzrAUZ/fU+b/WKqAzXskEAMApWDwOOyOoQ0iy01lrYE5PXXybP35yYpwkJ/rHOh9mWAoAwIEYlAI7I6hDSLICMnUEdZHi9dZKRZXH1EydwloDAICrMnUEdbAZgjq0us8qn/LLiCmr9Ad0Zg1KURiWAgBwMjXJu8iYqaP8EjZDUIcwLCDnSX5blF6qHahJCf4ySLOCeMovAQBOo6piqj21+n0GpcBuCOrQ6vJLeuoip7SiOqC3LTraP3W0LVF+CQBwS+ml+lWbltL2PexAaxDUodWDUsjUOXfxuE9uprH8kkwdAMC5Q1LSUxIkxqSLqECoCOoQkixD+SU9dc5dZ+CTY/h6k6kDADh68iX9dLAhgjq0uqdO1aEbJzTCeZm6nAwydQAA5zLuYGXyJeyIoA4hUQ3ExsoESjAjH9SZmqkz9NRVVHmlvNJ/LgAAnJSpY0gK7IigDiFRtebGH3r5BHURL780M1OnvtbG/gJKMAEAjt1RR/kl3BrUzZs3T+6//375yU9+ItOnT9deVlxcLE888YTk5+eH4yFg8b66AvrqHN1Tp6ZustYAAOCGoC4jlcmXsJ9WLb06fPiw3HbbbbJkyRJ9cWOUWqYlInl5eVpQ98orr8izzz4rw4cPD8+JYam+uq17irTbrDVwdk+db1jKwYL6YI5MHQDAqeWXWWTq4KZMXXV1tVxzzTWyePFiSUlJkR/84AfSoUMH/weOjpaMjAwpKiqSq6++Wvbs2ROuM8MijD/06Klzdqau4bCUQ2TqAABOHZRi2MULOD6oe+2112T9+vUyYsQImTVrljz++OPSpUsX/fX9+/eXL774QkaOHCkVFRXy4osvhuvMsAhjOR49dS7I1GWy1gAA4IZBKZRfwkVB3UcffaRl4/76179KdnZ2o2+Tmpoqf/vb3yQmJkbru4Nz1xrQU+fs6ZdKTro/U5dPUAcAcIgaj1fKKv2rmTJTydTBRUHd1q1bpU+fPtKtW7dm305l73r27Cl79+4N9aFgUVmG8gR66txQfsmgFACA8xSWVAfcJ1MHVwV1tbW1Qb9tXFyclq2Ds2Sn01PnqvJLQ1B3iEwdAMAhCkv9v9NSEmMlPo7nrHBRUKcycNu3b5fS0tJm366goEA2bdoU0G8H5600KCmv0coXED5VNV6p8dRaJlOXm5kU0FDu8QZ/YQcAAKsqKvVn6thRB9cFdVOnTpWamhqtp645f/rTn8Tr9cqUKVNCfSjYoPxSoa8uclk6KwR1xh7KujqG4wAAnKHQ0EKSkUpQB5ftqVPrDGbMmCFvvvmmtq/u7LPPlpKSEu11W7ZskY0bN2oTMpcuXaqtPFCLyeEscbHRkpYcLyXl9Ve41JP89tnJZh/LkUFdbEy0JJhcDqLKUQK+3kWV0j6LrzcAwN4KydTBzUFdTk6O/Otf/5L/+7//k9mzZ2vrC3zOOussfRl5cnKyPProowE77OCsvjpjUIfwKS0P7KeLiooSs6m+Ot/Xm111AADnrTMgqIPLyi+V0aNHywcffCBXXnmldOrUSQvifH9U0PejH/1I3nvvPTnhhBPCd2JYtq+OYSnhVVZpnHwZ8vWXCE7A5OsNAHBWUJdFUAebCvmZ4rZt27RVBSoDd9ddd2l/ysvLtRJMlZ1LS0sL70lhScY+q3zDD0WEd51BapI1xisbh6UQ1AEAnEAN//Kh/BKuy9TdeuutcvLJJ2vTLX1UMKeCPAI698gy/PAjUxdeZUfKHK0wJMUnxxDEs6sOAOAEhYagjvJLuC6o27VrlyQkJEhWVlZ4TwRbyTaU49FTF16lldbZUeeTnUGmDgDg3KCOTB1cV36pJlq2ZAF5a6lyzyeffFKbpqmmbXbs2FHOOOMMuf7667WztMRXX30lr776qqxZs0bbs5eRkaH1B1577bUybNiwY76/ep/zzjtPC2zVgJiuXbuKWxnLL1lpEF5lFR7rZeoCeurI1AEA7M1bWyfFxqCOTB3clqm79NJLZceOHfLCCy9IpK1atUouuOACmTlzprRr106mTZum9e89/fTTcvHFF+urFIKhJnHecMMNMn/+fG0hutq3l56eLp999plccskl2mCXY7nvvvu0gA6Bu+ryDXte0HqlFiy/bNhTp4YiAQBgVyVl1VJr+FVGpg6uy9QNHTpURo0apS0fV/voRo4cqQVciYmBC6mNfv7zn7f4cdSC89tuu00L4h566CE5//zztZdXVlbK7bffLnPmzJFHHnlEC7SOZcmSJfLMM89ovX/PPfecjBkzRn/dG2+8Iffee6/cc889MmHCBC0T2Jj3339fCy5xdKZONRp7vbUSE9OqoapoZPplqgUzdTWeWikpr5H0FGsMcQEAoDVDUuJjoyUpwRrTpoGWCvk7V5U9qr1Z6kr9nj17JC8vr8m3VW+j3jaUoO6jjz7SPv7kyZP1gE5RweMDDzwgJ510krz99ttyxx13aBm35qi3U1SZpTGgU1TGTwWIc+fO1bJ2V1111VHvr7Jz999/vwwePFgrB1WBpttlpfuvaKmkjapLzzH0XSE8e+qskqlTwaX6pVftqdVLMAnqAACO2FGXlmCJnbBAmwZ1Y8eOlbbw5Zdfan+feuqpR71ODWkZP3689jaqnPLMM89s9mOpQLB///7a+zSmd+/eWlB34MCBo17n8Xjkl7/8pXi9Xvnb3/4mF110Ucj/JidJjI+V5MRYKa/06H11BHWR2FNnjaBO/bJTX9+9h8v0EsxenTPMPhYAAK0fkkI/HdwY1L3yyivSFjZu3Kj9PWDAgEZf369fPy2o27BhwzGDumOVaK5cuVL7Wy1Sb+iJJ56QFStWyB/+8Act+ENgX115Zal2mwmY4VMWsKfOGkGdkpOZaAjqGJYCALAv1hnAKSxfOLx//37tb7X/rjGqj09pLLvWEqr0ctmyZRIXFyennHJKo714J554olam2RZUyarKCprF+NjHOofaVbfnYH1Qd6iwPGLnbsmZ2kokz2Qsv0xKiAn640f685RtaCI/WBDc19ttX7tQcSb7nsmq5+JMweFM7j2TccduRmp8WP5dTvw8RQJnCk6wQ+nCEtSpYGj27Nlan1lZWZm2YqBnz55ywgknyMSJE1v1sSsq6jMBTQ1g8b28Nf1tKst355136v12xiEpxcXF8qtf/Uqys7O1Hr62ov7dKjNoBatXr27+Dbz+z/26TTukQ2KB+WcyQTjPVFtXF1B+uXP7Zik7HGuJz5Onqli/vWlbnqxY0bJsndO/duHCmex7JqueizMFhzO560xbd+Trt6vKCsP+3Mspn6dI40yt16qgTu1r+81vfqNluRpGkqr35qWXXtICu7/85S/aLrhQxMTEBLUPL9TR6mpdghr6UlhYqGXibrnlloDX//73v9eGwDz77LNaYIejpSXF6LdLKqxxVcPuqj112uAZn8Q46zRupxu+3sXl/l16AADYTWml/zlmSiLTu2FfIQd1KtD6v//7P1m8eLEWUKmVBmoqZGpqqpbdUou9VcD09ddfy6233iovv/xySI+jsn4q4KqqanyxtVptoKg1BS316aefym9/+1stK6YGsajVCCqINE7LVG9z+eWXa/vs2lJSUlKTfYRtQaWcfVco1PoK4+eloe1FW+Tb9eu02zHxqTJixAjTz9RWInWmAwUq++mfKDt+7EiJDXJVRKQ/T+XRefLpsmXa7Zq6+KC+3m762nEmd57JqufiTJyJMzXvtXnz1LNJ7fbgAb1lxIgupp8pEjiTfc+kKgp9lYsRCereffddWbRokeTm5mpDRBp7YqfKMlVAp97ugw8+kHPOOafFj9O+fXstqDt48GCjA0x8vXTq7VriySeflH/+859aQKqCtt/97ncSHR34pPnPf/6z/hhq8mVjweSDDz6oBWCq167hmoTWUJlOK3wjKeoczZ0lJ9MfUBeUVLXJuY91JjOE80yV1bUB/XQJ8XGW+Ty1y07Rb6vBOC39+E7/2oULZ7Lvmax6Ls4UHM7krjMVllbrt7PTk8L+b3LK5ynSOFPTgl2zEXJQ995772kP8uijjzZ5pV4tJ1fZL7XzTb19KEGdylapCZibNm2SYcOGHfX6zZs3628XbIbxrrvu0oJS9YVSmborr7yy0bf19enNmjWryY+negmVSZMmhTWos5Nsw646Y8MxwrSjLtE6ky+VnHT/ygq1fLyqxisJceb/0AMAoCXUhf0iw566TMMgMMBuQg7qVKDVpUsXGTduXLNvp3bCqbfzrSZoqWnTpsnMmTO1wGr69OkBrysoKJCFCxdKQkJC0ANZ7r77bi2gU9m1v//971ofXXPpzqaoAK6kpES++OIL6dq1q7h9pYExU1dbWyfR0dbpAbOj0grr7agzLpxXF418PX9qrUHn3FSzjwUAQItUVHmk2uOvjGGlAews5I5QNeVSLf8OhhowovrsQqHWC6ig8KuvvpI33ngjoPxRlUyqbJpaBG4cYlJTUyNbtmzR/qjbPipbOGPGDC1D99RTTzUb0CF42en+oM5bWycl5f5SBoRhR11yvFiJ6u0zLmhVC8gBALCbQkOWTl2LTkux1u9boE0ydWo/3Pbt27WgSe12a0p1dbW26iAnJyekx1ErCx5++GFt1cC9994rb775ppYZW758udbrNmTIELn99tuP2m3nW0Tuy6SpxsfHHntMe5nqA1TBnfrTmClTpsi5554b0nndKDkxVuLjYqS6xqv3WXG1K4yZOouVXyo5mUlaVlYhqAMA2H3xeHpqgsRQZQQ3BnWq/PDDDz/UlnLffPPNTb6der1afdCarNjYsWPlrbfe0gayqKErqo9OBWoqQ3f11VdrEzKPRZVS7t27Vw/6VElnU1QGkqAueKq3UvXV7Ttc34NYUFwlvTqbfSrnZOpSksKyTjKsctITpb6bVSS/qGV76gAAsFqmzliBAthRyM8W1fCTjz76SJsieejQIW3YSO/evfXXb926VVtjoDJraqpkU8NIgtW/f395/PHHg3pbFfA17IcbNGhQsz1yLbVkyZKwfSyn9NX5grr8Yp7kt5Zx8bjVyi+VnAx/ye0hMnUAABsqMmTqCOrg2qDOV/aoplv+73//0/6oUkm1p05l5nwj/9VkIfV2atcD3NFXl1/c+E5BBK/U0JdoyfLLDP8ETDUoBQAAO2fqaBuB3bWqruu6666T7t27a1MkVX+dWoxnXI7Xq1cv+fnPfy6nn356OM4KC1MTEX1Ya9B6ZRUe/XZqsvWCutxMfxBPTx0AwI4KjJk61hnA5lrdrHPaaadpf3bs2KGVXKosnepxU6WYPXv2DM8pYa9MXQlP8sNZfmnJTJ1hVx1BHQDA9uWXBHWwubBMYFBZOhXA9ejRQ3/Z119/LQcPHtSGnMBlu+oovwxv+aXF9tQp2YaeOpWZVassmBoGALDvoBTr9a8DbbKnTlHTJC+55BJtfUBRUVHA615//XVtOMrFF18se/bsac3DwAaMT/LVSgOEc09dnKUHpaiAzni1EwAA+2Xq/L/XAFcFdWri5Y9//GNtX5waaa92xjXcYxcfHy8rVqyQn/zkJ1pZJtxRfqkyN2pADsKzpy7Vgpm65MQ4SUrwJ/oZlgIAsPegFDJ1cGlQp/bPqUBu1KhRMnv2bOnXr1/A6//4xz/KnDlztH12u3fvln//+9/hOC8sKstQi17tqZWySv+gD7SMx1srldX1i9yt2lOnMCwFAGBX1TXegOcqmalk6uDSoE71zCUkJGi74zp16tTo2+Tk5GgrD2JjY2XWrFmtOScsLj0lXmJj/D1VTMAMT+mlVcsvFYalAADsqqjU37uuZKaRqYNLg7q8vDxtwmVubm6zb9ehQwdtiIrK1sG5VAmusR6dvrrwBHVq9khifFjmGUW0j5LySwCAnRSW+p+npCTGSlxsjKnnAUwL6tTagvLy8qDeVvVXqWwdnC2bXXVh76dTvWvRFp0qaRyWQqYOAGDXTB3rDODqoE4tFt+5c6esX7++2bfbsmWL9kdl9eCetQZk6pw7+dInN9NYfkmmDgBgH4WGnboZqQR1cHFQd/bZZ2sZuJ///Oda0NYYtZBcvV5Raw/gogXk7KoLS6bOijvqfHIMX28ydQAAOykw7qgjUwcHCLkm8sILL5QZM2bI999/L+ecc442BXPAgAFaWWZZWZls3LhRli1bJh6PR/r37y+XXnppeE8Oy8lqsNYAYcjUWTmoyyBTBwBwQPklmTq4OaiLi4uTp59+Wu68806ZP3++LF68WJYsWaK/3renbOzYsdoETDUpE+7pqcs3lDUg9KDO0pk6Q09dRZVXyitrtB5AAADstKOOoA5O0KrpJWrB+PPPPy+rVq3SdtKpHrvCwkJJSkrSJl5OnTpVxo0bF77TwtLI1EWg/NLCQZLqQYiJjhJvbZ1egklQBwCwg6JSyi/hLGEZSTls2DDtD9yNnrpIDEqx7t4cNZVTrTU4WFChl2B265Bm9rEAADimQkNQx6AUuHpQCtBcUFdR5ZHKKo+p53HGoBRrrwIxDks5VEh2FgBgw/JLMnVwgBY/Y1RDUDZt2iRDhgw5avec6q17/fXXZdu2bZKdnS3Tpk3TBqSo4SlwPnWlS61UO1KNp/XVdU5INftY9s7UWbycsX5YSoF2+3Axw1IAANan2gaKywjq4OKg7oknnpAXXnhBKisr5fPPP5cuXbror3vmmWfkscce04ekqMBOTb9855135LnnnpOuXbuG//SwFNVfpQI735jgguIq6ZxLUNeqQSkWLr9UcjJZawAAsJeSsmr9ArTCoBS4Kqi7//775b///a8+1bKkpER/nQreVECnXpeamiqXXXaZNkTl448/1l536623ausPoqKiIvOvgKWGpfiCOhaQh6a0otoWKw2UnHT/WoN8gjoAgM2GpMTHRktSgrVbHYCw9dStWbNGC+iio6Pllltuka+//loGDhyov94X0KmgTa05uP322+Xyyy+X1157TSvBXLduncycOTOoA8E5fXVMwAxNWYXHFtMvG641YFcdAMBu/XQZaQkkHeCeoO69997T/r7rrrvkpptukvbt2+uvy8/P1/bTqf8QkyZNkjFjxuivUy/71a9+pQV8n376aSTOD4vJMtSlk6lrOfV/xTgoJTXZPkHdITJ1AAAbKDCuM6D0Em4K6hYuXCiJiYly4YUXHvW67777Tmpra7Xbp5122lGv79Onj3To0EHL1sFtaw14kt9SVTVe8Xjr/z9Zffm4f1CKv5zFeHYAAKyIHXVwbVB34MABbZl4fPzRQxsWL16s3544cWKj768ye4cPH27NOWHLBeTsqmvNkBRb9NQZMnWq3ZZAHgBgq3UGZOrgpqCuvLxcEhIa/6ZXpZe+wK1bt26Nvo2altnU+8NZstMN5ZclPMFvKWPpZVxstMTHxYiVqfOlGSZ0MiwFAGB1ZOrg2qAuKytLCgrqd1EZHTp0SDZv3qz1zo0fP77R962pqZHdu3dLZmZm608Lm2XqeILfqnUGFs/SNd5Xx7AUAIC1+aZ0K2oVE+CaoK5///6ya9cuOXjwYMDLP/vsM33FwdSpUxt9XzUps6KiImBaJpwrO83/BL+kvEZqPF5Tz2PrxeM2DOrYVQcAsLpCBqXArUHdKaecog1DUbvqVOZNKSoqkpdeekm7nZKSIieddNJR71daWiqPPPKIlsmbMmVKuM8OC8oylF8q9NWFXn5pl0xdbqZ/WApBHQDAVuWXBHVwU1A3ffp06d69u8yePVubcKnWGvzwhz/UyipVwHbjjTdKUpL/iV11dbXMnTtXW0K+detW6dixo5x77rmR/HfAIuJiG/RY0Vfn/PJLQ8ktu+oAAFamKswCBqXQUweHiA3mjeLi4uT555+X6667Tnbs2CF5eXn6684++2y55pprAt7+1FNPlf3792v/cVSw99BDDzEoxWXDUkrKq7Xb9NU5v/wy27DWgEwdAMDKKqo8UuPxr98hqIOrgjpFZeo+/PBD+eSTT2T16tVaoKdKKhtbY5Ceni779u2TkSNHyp133inDhg0L97lh8WEpO/aVaLfzKb90fPllYE8dmToAgHUZs3TRUSKphuoiwBVBnaICuXPOOUf70xyVmVOBXdeuXVt7Pth8ATmZOudn6hr21KkMvSrLBgDAypMv01MTJEZFdoDbgrpgDRo0KBIfFjaRZShlYBl16Jm6VBtm6lRJi5p6mp7ClU8AgPUwJAWuHpQChJqpI6hz/qAUFXzGx/p/lFCCCQCwKtYZwKkI6hDhBeT01IWeqbNHtkuVWuYwLAUAYANFTL6EQxHUIbKZOlYatGJQSkSqoyMim2EpAAAbKDBk6jLI1MFBCOoQ0QXkqnbd6/WPDobzyi+VXDJ1AAC79dSRqYODENQh7LLT/FmburrA+nU0rba2Tsor7Vd+efRaA4I6AIA1BSweT7XP71ngWAjqEHaJCbGSnOgvHaSvLjjlVR4tCLZjps4Y1B2i/BIAYIegznARGrA7gjpERJbhByV9dS0vvVRSDIGx1RkHpeSTqQMAWBQrDeBUYQnqamtrZfXq1fL+++/Lq6++qr2spqZGdu3aFY4PDxtiAXnrgrqkhFiJibHPNZecTAalAACsrbrGK2WVHv0+g1LgJK1OBcyYMUP++c9/yv79+/WXXX755ZKXlydnnnmmnHHGGfLnP/9ZEhL4j+PWYSn5lF8GpbSi2pall0pOuj9Tp5aPV9V4JSEuxtQzAQBgVFTq/z2rZKbRUwfnaFVQ9+ijj8pzzz0ndXV1Eh0drf3xer3a6/bt26fd/uijj7SA78UXX5TYWPuUk6F1yNS1LlOnFnrbLYiPiqofjOPL1nXOTTX7WAAA6ApLKwNaHOJiufgI5wi5vuu7776TZ599VhITE+W+++6TRYsWybBhw/TXjx8/Xv7yl79IUlKSLFmyRP73v/+F68ywW08dQZ2j1xkosTHRAb0JTMAEAFh7SAoVZHCWkIO6V155RaKiouSBBx6Qiy++WFJTj74qf84552iBncrkzZw5s7VnhY1kB5Rf8gS/pYvH7ZapU3Iy2VUHALDLjjomX8JZQg7qVqxYIbm5uVrPXHNOOeUUad++vWzevDnUh4INZVF+2aqgzm6ZOiXH8DXPZ1gKAMBiCgyZugx21MFhQg7qioqKpEOHDkG9rXq7ykqe2Lu2p66kSlusjeaVlds8Uxewq47/7wAA6w5KYZ0BnCbkoC4zMzOolQWq9HL37t2SlZUV6kPB5pk6b22dlJQHTpzC0UorbZ6pM+yqY60BAMDSPXUEdXCYkIO6UaNGSXFxsTbdsjnvvvuuFBQUyMiRI0N9KNiQmioVH+v/9qKvztnTL5XcgF11fL0BANadfsmgFDhNyEHdFVdcoWXh7r//fvniiy8aXUj+1ltvaa9XA1XUMBW4h/qaZxvK8QrYVXdMpeU2z9QZdtUR1AEALF1+SVAHhwl5cdzYsWPl2muvleeff15uvvlmSUlJkZqa+ielP/rRj2T79u1SVlamBX4XXXSRTJo0KZznhk3WGuw7XK7dJlN3bGU2L780BvHq663KbmOio0w9EwAAjZVfZlB+CYdp1TbwX/7yl9K1a1f55z//KYcPH9ZfvmbNGu3vtLQ0uf766+W6665r/Ulh82EpBHVOL780DkpRg3HU6Gjj9wAAAGZRFxqLy9hTB+dqVVCnqLLK6dOny/Lly2XTpk1SUlKiLRzv1auXls1Tt+FOWeyqc9VKg+TEOElKiJWKKo8+LIWgDgBgBSVl1WIcxM2gFDhNyEFdXl6eJCQkSE5OjsTFxcm4ceO0P43ZuHGjNgHzpJNOas1ZYedMHT11zarx1EpVtVe/n5pkz/05aljKrv2lel9dv25mnwgAADUkxf88RA1yUxchAScJeVCKCtB+/vOfB/W2d911l/YH7uup8yFTF3zppZKSZM9fNgxLAQBYUZFxnUFagjbQDXCSoJ85qmmWPmr4ie9v35/GqJerjJ7aZ8fycXdn6gjqgh+SEh0dZdsriMZhKeyqAwBYRYEhU8eQFDhRUM8ct2zZIueee654vf7yMHWFY9myZTJo0KCgHmjgwIGhnxK276krKK7UgnyujB07U5eSGGfbz5NxWAqZOgCAVajhXT4MSYFryy/79OmjrSVomJkz3m/uT2JiovziF7+I9L8FFs7UVXtqpayyfoAGmt9RZ8fJlz65mcbySzJ1AADrrTNgSAqcKOgarzvuuENOP/107bYK1K666irp37+/3H333U2+T3R0tCQnJ0vPnj21v+Euacnx2p4yNUbYl62zc8DSZpk6m/bTKTmGQJ5MHQDAkkEdmTo4UNDPHlNTUwOmW3bu3Fl69+7d5MRLQPWGZaUlyKEjT+5VX123DmlmH8uSSg09dXadfKnkZJCpAwBYe/olPXVwopBTAq+88opWVhkMVhq4lxqc4QvqVKYOjSstr7b1jrrGeuoqqrxSXlmj7a8DAMAqQR3ll3CikFcanHLKKaw0QAvXGrCrLpjyy9Rk+wZB6uqnKrn1oQQTAGAFDEqB00W3ZKWB74+agmkchGJ8XcO3U+sM1J+KCkqxxO0LyEt4gt8U4xAZNf3SziW3rDUAAFiJeq7KoBQ4HSsNEFFZ7KpzVfmlb1jKwYL6YO5QIV9zAIC5yis9UuPx71smUwcnYqUBIio7YFcd5ZdOL788alhKMZk6AIB1Si9Vh0Bqsn0HkgFNYaUBIopMXXBKGywftzMWkAMArKTAUHqZ3qD3G3AKVhogorIDBqXwBD+4PXXOydTlE9QBAKw0JIV+OjhUyCsN5syZE96TwJGyDOWXFVUeqazySGKCfZdrR0pZpZPKL/2B/CEGpQAATMY6A7hBWJ5dFxUVybfffitbt26VkpIS+c1vfiNVVVWycuVKMnkup354RkWpkt36+/klldI5IdXsY1mKKmcuLaf8EgCASAiYfMmQFDhUbGufjP7zn/+UF198USor/U/eVFCnlo2rvrsRI0bIk08+KdnZ2eE4L2wmJiZa213m+4GqhqV0ziWoM6qq9oq3ts6fqXNQ+aUqefF4a4XuBQCAFTJ16jkJ4EQhLx9Xfv3rX8tTTz2l7aDLzMyUpCT/k7nCwkIt6FuxYoVcccUV7KlzMfrqgi+9dEZPnf/rrTK0fM0BAGYiUwc3CDmomzVrlsycOVPLwD333HNa+aVxF93o0aPltddek5ycHK0s8z//+U+4zgybMS6jLuAJ/lGMpZfxsdESHxcjdqbOn2YYF82wFACAmRiUAjcIOaj73//+py0gf+SRR2TKlCmNvo0K7B577DEtY/fZZ5+15pywsSzDVTGyNs2vM7D7kBQfhqUAAKyCTB3cIOSgbs2aNdKpUyeZMGFCs283ZswY6dKli2zfvj3Uh4LNZRt21Rl3xeDo8ku7l176MCwFAGAVTL+EG4Qc1JWXl2t9dMFQJZoejyfUh4LNsYC8eU6afOmTm+nvryWoAwCYpbrGK+WV/uegDEqBU4Uc1OXm5sqOHTu00srm1NTUaFk69fZwp2zDrjp66ppfPJ5q6EWzsxxDIH+Y8ksAgAWydEpmmjN+zwJhC+rU/jmVrXv99debfbuXX35Z212nyjDhToGZOsovm+upc0qmLtuw1oBMHQDACkNSVItDXKy9h5EBYQ/qrr76aomOjpaHH35Ym2xZUFAQ8PrDhw9rQ1IeffRR7e0uv/zyUB8KDlppUFJeLTUer6nnsXKmLiWpVasjLdpTR6YOAGCBISmpZOngXCEHdWp9wV133aWVVz744IMyadIkWb58ufa6iRMnyvHHHy/PPPOM1NbWyi233CLDhg0L57lhI1mG8kvfAnI4u/yyYU/dscq0AQCI/ORL/wVHwGlatXz8sssuk6effloGDBigPWnz/VFZO/V39+7dtUzdz372s/CdGLajSh3SDKP680soxzMqrah2XPmlMVNX46mVEsMwGAAAzOipyyBTBwdrda3X1KlTtT979uyRTZs2af1zSUlJ0qtXL+nTp094TglH9NX5ntgzLCVQWYXHcSsNUpPitEXq1Z5a7T59dQAAM7DOAG4RtgYetYtO/QGa6qvbua9Eu82wlObKL50R1EVFRUlORpLsPVym99U5o1sQAGAnRSX+ahjKL+FkrSq/BIKVbSjHI1PXdPllqkPKLxt+zdlPCAAwQ2Gp//cPg1LgZCFfPD/uuONafOV+7dq1oT4cbC4rzV/ywBP8ZqZfOiRTp+Q2WGvQvr2pxwEAiNsHpVB+CecKOahr6TQ7pt+5W7ZhV12B4Qes29XW1kl5lSegF80pAtcaVIoQ1AEA2lhRqb8aJoOeOjhYyEGd2k3XlIqKCjlw4IDMmjVL5s2bJ7fffrtce+21oT4UHLeAnEydT3lljRivdzhlUErDoK7+a84vUwBA2/HW1klxGZk6uEPIQd24ceOO+TYXXnihPPLII9oS8iFDhsjkyZNDfTg4KVNHUKcrNZReKskO6qlTg1J86qdf8ssUANB2SsqqpdZw4ZTpl3CyiA9KuemmmyQlJUVeeOGFSD8UbLKAvKi0Srze+lH3bmcM6pITYyUmOkqcIieT7CwAwBrrDNSanaQE5jDDuSIe1CUmJmpLyFevXh3ph4LFVxr4qKtmxh+0bhYwJMVBpZdKTro/U6d2FNZ46KsFALSdogZDUtTQPsCpIh7Ueb1e2b9/v9TUBJaZwV0SE2IDrpAVsKvu6B11DgvqVHbW+PuzuMJr5nEAAC5TYLiAzJAUOF1EgzqPx6P11B0+fFj69OkTyYeCDWQbSjDzSyjHa1h+6bRMXWxMdED/Qkk5QR0AoO2wzgBuEnJx8WWXXdbs+oLq6mrZtWuXFBcXa+nuCy64INSHgoMmYO45WKbdZlhKI+WXDhqS4pOTmaSvsCBTBwBoS6qH34chKXC6kIO6pUuXBv2255xzjlxyySWhPhQc2FeXT/nl0eWXDlo87pOTniibj9wmUwcAaEtk6uAmIQd1N998c7Ovj4mJkaysLBk9erT069cv1IeBg2Qb9paRqXN++WXDXXXFBHUAgDZkHMpGpg5OF7GgDmgoKyBTR1B39KCUeHEa4646yi8BAGYFdQxKgdNFfPol0NiglAIGpTSSqYt1dKaO8ksAQFui/BJu0upnkaWlpfLqq6/K7NmzZdu2bVJeXi7JycnSo0cPmTp1qlx11VWSmZkZntPC9oNSfOipc/5KAyWXTB0AwARqaB+DUuAmrQrqNm7cKDfeeKPs3btX+8/jU1ZWJmvXrpV169bJe++9J0899ZQMHDgwHOeFjWUbgrrCkkrte8bti0BLHV5+aeyjLKnwSq3aPA8AQISVV3qkxlOr3ydTB6cLOagrKSmRG264QQvocnNzZfr06TJkyBBJTU2VoqIiWbNmjRbQqdffdNNN8v7772uvg3sZM3Ueb50Ul1W7vsa9rKLaNYNS1HWfsir/L1gAACLFmKWLjhJJS3behVMgLEHdyy+/rAVsI0eOlGeeeUbS09MDXn/66afL9ddfr/1ZuXKlvPHGG3LttdeG+nBwgJTEWImPjZbqI1fO1P4ytwd1pRUeRwd1yYlxkpQQKxVV9f9OJmACANqCb0eqkp6aINEqsgMcLORBKaqHTq0t+Otf/3pUQOejXq5er0rsPv3009acEw6gvg8C+uqK3D0spcbjleoar6N76pTczMASTAAAIo11BnCbkIO6HTt2SO/evaVr167Nvl23bt2kT58+snPnzlAfCg7tq3P7WgNjP51TM3VKTrphWAqZOgBAG2BICtwm5KBODbmIiwvuSWhsbKzU1AQ+gYU7ZbHWoNHJlzHRUZIYHyNOZByWQlAHAGgLrDOA24Qc1HXp0kU2bdok+fn5zb6der16u06dOoX6UHCQbBaQNxrUqSydUyeBBuyqo/wSANDW5ZcEdXCBkIO6E044Qcu+3XPPPeLx+Ic9GKmX33333eL1erWddYAxa1Pg8l11gYvHnVl6qeRmUn4JADAvU+f2oWxwh5CnX/7kJz+Rt99+W7744gttncEll1wigwcPlrS0NG3dwffffy+vv/66lqVTqwzU2wNZZOpcs3jcJ8fQR0mmDgDQ5uWXBHVwgZCDug4dOsjjjz+u7aDbsGGD/OEPf2i07y4lJUUee+wx7e0B46AUt/fUuSVTl5NBpg4AYOKgFMov4QIhl18qEydOlA8//FAuuugiad++vRbE+f6oheTq5WoB+eTJk8N3YjhmUEp+cZX2veJWDXvqnMrYU1ftqZPySoYmAQAii5UGcJuQM3U+nTt3lvvvv1+7XVZWJqWlpVp2TpVcAs1l6tSOtvJKj6MDmua4pfxS9TKo6Z7e2voA/nBRpaSl+L8PAAAIJ9/zCx8ydXCDVmXqGlLBnCqzJKBDU9KS47Un+D5u7qsrdUlQFx0dxX5CAIApWTolIzXetLMAtsnU+RaRq9UFlZWVxyzXhLupJ/hZaQlyqKhS76vr1iFN3MgtPXW+qacHCyu0276vPQAAkR6Son6/xsU6cw8sELagTvXLPfLII3Lo0KFjvq3awbV27drWPBwcIis9UX9in+/iJ/huKb9sOAHTzV9zAEAbD0khSweXCDmoW7Bggdx5551BD7pw80AMBAosxXPvrjq3Zep8VE8dAABtss7AsEoJcLKQg7oXXnhBC9RGjhwpv/71r2XAgAGSnJwc3tPBsZk6HzevNQjM1Dn7SmLHbP/PhtVbDmk/O1T2HgCAcGPyJdwo5EEpa9askaSkJPnXv/6lBXYEdAhWtmEKlZuHZgSuNAhLe6tljR3k31O552CZbNlTZOp5AADuCOoYkgK3CDmoq6iokJ49e0pWVlZ4TwR3ZepcWn6pMlVuKr/skJ0s3dr5f7HOXbbb1PMAAJyL8ku4UchBXdeuXaWoiKvtaF1/lVszdZXVXqk9srfNDeWXytAe/mz+18v36HvrAAAIJwalwI1CDurOOOMM2bt3r8yfPz+8J4LjZRuumrm1p6603J+lc0P5pTK4e5L4VhSqYP77rceemgsAQOsydfTUwR1CDuquueYa6devn/zqV7+Sjz/+WKqq3FlGh5bLSvf/gC2v9EhltUfcpqzSH9TFx8W4YodOSmKM9OnkD+i/WkoJJgAg/IpKq/XbmamUX8IdgkoPTJs2rcm+OlWC+Ytf/EKio6MlPT1dEhIavyKiJt19+eWXrTstHEFNolKDD31bLlRfXadc52eq3LqjrmEJ5qa8+uzsglV58rPpw1wR0AIA2oYq7S8uMwxKSaP8Eu4Q1DPpffv2HXPog9frlYKCgibfhvHl8ImJiZaM1AS9PEKV4nXKTRE3KS2vds2QFKMBXRMlIS5Gqmq8UlbpkSXr9svEoZ3NPhYAwCFUQGds2WalAdwiqKDuwQcfjPxJ4Lq+Ol9Q58a+OmP5pZsydQlx0TJucAeZtyJPuz932R6COgBAREov42OjJSnBXZVAcK+gvtPPP//8yJ8E7uurq39eL/lF7gvq3LTOoKGpI7voQd2itfu0UlS3fQ4AAJFRaLhQrIakUCkGtwh5UArQGtnp7l5rUFbuzkydMqJ/O0lLru9xqPHUyrer95p9JACAQxQaMnWq1QNwi6Aydd9++21YHmzixIkhv++2bdvkySeflKVLl8rhw4elY8eO2lqF66+/XlJSWtaP9dVXX8mrr74qa9askdLSUsnIyJDRo0fLtddeK8OGDWt0IMyLL74on332mezYsUNqa2u1PX2nnHKK9j5qQAxasYDcMHrYLUpdWn6pxMZEy/EjOssnC7bri8hPGdfd7GMBAByAdQZwq6CCuquvvrrV6Wv1/mvXrg3pfVetWiVXXXWVlJeXy/Dhw2Xo0KGybNkyefrpp2XOnDny+uuvS1paWlAf69FHH5VnnnlGO8/gwYO14HDr1q1awPbFF1/In//8ZznvvPP0ty8sLJQrrrhCNm7cqAVvI0eOlJiYGFm9erX2cdQ6h9dee006dOgQ0r/NrbINP2jdmKkz7qlzY+nh1JFd9aBu1eaD2veAMXsLAG0p72CpvPddvnTKipfhww1TNmDv8ksydXCRoMsv1YTL1vxR2a1Q1NTUyG233aYFdA899JC8+eab8vjjj8vs2bPlpJNO0oKtRx55JKiPtWTJEi0QS05O1jJ1M2bM0LJ/n3zyifzhD38Qj8cj99xzT8C0z7/+9a/aY4wfP15mzZqlZeyef/55+fzzz2XKlCmya9cuuffee0P6t7lZQKbOhUGdcaWBG4O643pmS/usJO22mlI2b8Ues48EwMWeeW+NrNhaLp8sLZRZC3eafRyEa0cdmTq4SFCZuvXr14tZPvroI9mzZ49Mnjw5YGBLYmKiPPDAA1pg9/bbb8sdd9xxzDJI9XaKKpkcM2ZMwOsuvvhiLes3d+5cLWunMoOVlZXy4Ycfaq9XAWVWVpb+9uqxHn74Ye1c6n3Uvj5VxongZGcYe+rcV37p1umXPtHRUXLCyK7y9pxNegnmuSf0MftYAFzI462Vddvy9fsvzFwrIwa0l865qaaeC6EpLDWUX5Kpg4tYflCKb2H5qaeeetTrVJClMmgqmzd//vxjfiwVCPbv3197n8b07t1b+/vAgQPa36p3T5Vojho1Sjp3Pnrsek5OjhbIqSzkoUOHWvxvc/tKA5+S8mptYIabuL38Upk2qqt+e9OuQq38CQDa2q79JVJt+B2k9mg++voy8Xrd9XvJiUEdg1LgJpZf3qFKH5UBAwY0+vp+/fppgd+GDRvkzDPPbPZj3Xfffc2+fuXKldrfnTp10v7u0qWL1q/XFDU0RfXcqR679u3bSzj5FrqbxfjYkThHekrgt97hwnJpd6Qcz6wzhSLUMxnLL5MSYsL677HL56lr+xTp0TFNduwr0e5/uXSXXPyD/qaeyWycyb5nsuq5ONOxbdzpz9L5bNhRIG99sVEuPLmfmMVqnye7nMnYU5eeEmfKOe3webICzhR8TBC2oO7Xv/61NljkV7/6leTm5uovawn1/qpcsaX279+v/d3UIJJ27doFZNdCpUov1fCVuLg4baplMP72t79pf6sSzGAHtQRLTdxcsWKFWIEaChMJSfHRUlFdfyV04dJV0jU3wfQztUZLzlRc5v+ls2fnVqkt3W36mdqK8Uz9OkbJjiMtrLO+2yoDcstM2Slk9c+TVXAme5+LMzXuuxUF+u3oaBHfCID/ztogKVEF0jm7fgWL2z9PdjiTGqxn7NPft3tbxH6/2vnzxJnse6ZWB3UffPCB9kTrxhtv1IM638uOFT363ibUoE4FN77Sycb4Xq4GqYRKZfnuvPNOvd9OTcQ8FjV5Uw1OUY//y1/+MuTHdrPUJH9QV1LhnjKX2to6qarx/79JjLd8FXTEDOmRLLNXFGu380s8kpdfI11yzH8CBcA99ub7B2ucPCxDFm0slaJyrzbE6Z0F+XLD6R0kLpYF1nagfrcaq2ZTEt37+xXuE1RQp0b8q6DMmI3yvSzSVGljMJMzg01NNnZVR+26U2WUJ554otxyyy3HfJ9//OMf8q9//Uuio6O1YS1NlYa2RlJSUkQ+brBUytl3hUKtkFBfh3DrvPg7OVhU34uYmdtJRozoafqZWiqUM6keQhH/tMexo4eHdViK3T5Ps1YtkLVHhhTsK0uRH5482PQzmYUz2fdMVj0XZ2qe6uc+8Oan+v1eHRPk+LED5L7nF4l6WnGo2CMr8+Ll2nPa5ueSVT9PdjlTl+591YIK7XZ0lMjEcaO0wVxmnsmKnyfOZK8zqeSTL8nV6qBOTX4M5mWRoBaLq4CrqqrxCYlqQqWi1hS01Keffiq//e1vtU+UGsSiViM098Wrrq6Wu+++W95//32tTFN9Dn74wx9KJKiA2QrfSIo6RyTOYtxLVlhS3aLHiNSZWiPYM1UeyU4q6rpIWnJCxH7p2OHzpAam+IK6+Svz5Jpzh0pMG/8StsPnyQo4k73PxZmOtn1viT6oKyZapH1GnAzv116bxvve3C3ayz+cv03GD+4oI/qHt3feTp8nu5yppMKj305PTZC4OPNHR1jx88SZ7HWmYJNols9L+waQHDx4sNHX+3rpWjqoRO2nU/vvVEB3+eWXa9m3+Pimy77UJMwrr7xSC+hSU1O1fXdnnXVWix4TTQd1blpAbpx8mZwQa8pVRCuZPLyLHsQVlFTJ6s2N/18HgHDbvLtIv90+M05iY+p/Fl1xxnHSvaO/OukfbyyXUq3KAlbGOgO4WdiDunXr1snHH38s3377bVCpwmPxlSBu2lS/z6qhzZs3B7zdsahSTpWdUwvMVfnk7373O/n973+v3W7Kzp075cILL5Tly5drEzHfeOMNbTgKwriAvKTKnYvHk+kfS0+Jl1ED/Rdl5i5jETmAtrF5d6F+2zgQJT4uRn5x6Wg9yDtUVClPv2OvoQluVGQM6lg8DpdpUVBXUFCgBUNqmIhatt1wSqVa4H3BBRfIL37xC/npT3+q9ai9+eabrTrgtGnTtL/VUJLGzrNw4UJJSEiQiRMnBvXxVPnku+++q/WsqWydyr41R/271NuoBeiqtvatt97S1iggvLvqXJWpMwR1qYnu3FHX3M66BavzpLrGGmOEAbgpqAv8edy7S4ZcetpA/f7c5btl3nIuOllZoeECMZk6uE3QQd3atWu1/rGnnnpKvvnmm4Bl26qv7YorrtD2vKmBJbGxsZKenq71wt17773y/PPPh3xAtV5AZce++uorLUNmfEyVZVNTLy+66CLJzs7WX6eWkW/ZskX7o277vPfeezJjxgytPlb9O1TQeSxqjcPevXu1peUvv/yytnAc4ZGV7v+BaxxB7Kagzq2LxxsaN6ijJMbX162XV3pk8br6VSYAECmql257Xv30XaWx1QUXnNhPjuvpf37xrxkr5XBR66uQEBlFpf4SWTJ1cJugOkjVkJKbbrpJ8vPztaDm5JNPlszMzIDx/qpEUTXyqcDv/vvv1waczJ8/X26//XYtu6eCs549m59u2Bi1MkCtQlDZQRUgqsxf165dtVJI1U83ZMgQ7TEaZtd8i8i/+OIL7e3VNJvHHntMe5lay6CCO/WnMVOmTJFzzz1XC15VJlBRQap6/Kbccccd0rlz5xb/+9wsOyMxoGTCW1vX5gMyzC6/TE0mqFMSE2JlwtBO8tXS+n1Cc5ftlsnD+P8EIHJ27CsWz5H593Gx0dIu4+ifx+p30h2XjpJbH/lSKqq82kU51V/3h+snmrJTE8H31GWQqYPLBBXUvf3221q2asSIEdoof2NWTGXmVEmiogK+Bx98UB84cvzxx2v9ayqjpgIoVZYZirFjx2qP8cQTT8iiRYu0PjoVqKkM3dVXX60FkMGMA1X/Bl/QN3PmzCbfNisrSwvqvvzyS/1lS5YsafbjX3PNNQR1rSi/VPuAVGBnHJ7iVKUV/iuJKZRf6qaO7KoHdYvX7teePIVz1QMAGG3e5S+97NkpTe+fa6hjTopce+5Q+eebK7T7yzcelI+/2SY/PL53m50VIfTUEdTBZYIK6lTpo7oipXayGQM6Re1yUJMh1evPOeecoyZInn322fKHP/xB5s2bF3JQp6jyR5XxC4YK+FQQZzRo0KCjXhZM/536g8hlZ5ISYqWiyqP31bkhqAsYlELQohvRv51kpMZr5TPq6vmCVXly6vgeZh8LgAv66fp0VdVHTffy/mBcd1n0/T5Z+P0+7f4LH66V4f3bSdf2/gmZMB/ll3CzoHrq1OTJTp06Se/eR1+V+u677wLKFhtSQV6PHj30LBlglO3Cvroywx4dyi/9YmOi5fjhXfT7qgQTANokqOuS0ezbqgvXN184QrvwpKhhTo++vkwv34Q1sNIAbhZUUKd66ZraA7d06VLtbzUcZdSoUU32xamBJkBzaw3yi92x1oDyy+CmYK7ecoiBBAAiosbjlR17/UNS+mqZuuapzI8K7Hw27SqUN2dvjNgZ0TI13jpt0JYPmTq4TVBBnVoZUF1d3ejONxXUqStYaty/ervGqPLMjIzmr4LBnYx9dQUlbsnUMSilKQN6ZEmH7GTtdl2dyLwVjA8HEH7b96ohKXX6kJRuHVKDer8JQzpppZg+/5u9UTbuLIjYORG8ssrA8llfVhVwi6CCug4dOsiuXbu0oShGy5Ytk9LSUu32pEmTGn1ftd8tLy9P+xhAs5m6IpcEdZX01DVFXSCaasjWfUUJJoAID0np3TlDK/8O1rXnDtEvPtXW1skjry2VyiO94TBPWWVtwO/WuNj6NTmAWwT1U2zChAla8DZ79uyAlxtXAqiVBY1Ru93UE7Xx48e39qxweE+dWxaQl5YbgjrKL48ydaS/r27L7iLZtb/E1PMAcJ7Nu4v02326tqySKDkxTm6/ZJT4NhrkHSqTFz/8PtxHRCsydfTTwY2CCuouvPBCLTD7zW9+o+2JW79+vba8Wy3zVi8fPXq0DBw48Kj3U69/7bXX9P11QHOZOsovoXTvmK5dOfeZu5xsHYDIZer6dTt2P11Dg3vnyAXT+ur3P16wXZau3x+286HlSg2ZOvrp4EZBBXUDBgyQG2+8URt2ohZwn3/++dp6AVWOmZycrK0sMHrmmWe0QPDOO+/Uln6rnW+DBw+O1L8BDumpc8OgFDUxrdrj/8XDHrbGTR3lz9Z9vWzPUaXfANCan8Nq8bhP325ZIX2cy04fKL06p+v3H//fcikuO3r+ANoGmTq4XdBF5Lfeeqv8+c9/1nbAqSdY6o+advnqq69Knz59At72nXfe0fbXqbc57bTT5I9//GMkzg4HyDKUXxaWVDr+ybsxS6dQftm4E0Z21Uub9h4uYxABgLAOSfHW1v+uiY+LkW7tgxuS0pDq2frFpaP1fjx1YfJfM1Y6/veYHXrqGJICNwpq+bjP9OnTtT/FxcXaCgOVpWuM6p8bOXKktoy8qQEqgJKdkaTfVpPI1FXODAdfYSs1BHWxMVGSEE8jd2NyM5O08qY1Ww5r9+cu3yMDemSbfSwADqBWEfj07pwuMTHRWlVRKHp0SpcrzzxOXphZ31P3zco8mTt4t0wb3S1s50VwSo2ZOkMVEOAWwY97MkhPT28yoFPuv/9+eeihhwjocEwpibESH+v/NiwoqXJNpk5N51L9pjj2zrp5y/eIlyW/AMJgi2HpeDD76Y7l3BP6yNA+ufr9p99ZJQcL2LFpZqaOnjq4UUhBHRAuKqgJXEDu7GEpxkwdpZfNmzSss5bNVApLq2Tl5kNmHwmAwzJ1fUMYktJQdHSU3HbxSElOrC9+Kqv0yGNvLNPWHcCsnjrKL+E+BHUwXbZxAqbDgzomXwYvLTleRg/077ecy846AK1UVeOVnYY1KeHI1Cnts5PlhvOH6vdXbT4kM+dvDcvHRgjTL1Mpv4T7ENTBUsNSyNTByLiI/NvVedoTMgAI1ba8Ij2Dpnqau4Y4JKUxJ47uJhOHdtLvv/zR2oApm4gc9TUtrzIMSkkjUwf3IaiDpdYauKmnLjWZXzrHMm5wR0lKqB8mU1HllUXf7zP7SAAcsp9O7cNUQ1LC2U5w04+G6/1cNZ5aefT1ZdrfiCxjQKew0gBuRFAH07m2p44ddceUEBcjE4d21u9TggmgNTbvDm8/XUNqevPPfzxSv791T5H8d9b6sD8OApUZgjo1fC0poUXD3QFHIKiD6bKN5ZdFle6ZfnmkqR7Bl2AuXb9fSspZ7gug9Zm6cPXTNTTmuA5y+sSe+v0ZczbJum35EXksNLbOIIHJ0nAlgjpYKlNXUOKeoI7yy+AM75url9KoXYYLVuWZfSQANlRZ7ZFdAUNSMiL2WD89e7B0yk3RbqsWvkf/u1QqqjwRezy3K6tgnQFAUAdLTb/ML66SujrnjoEurfBnmSi/DI7qeTl+hLEEc4+p5wFgT9v2FGsBlpIYHyNd2qdF7LFU+d8dl46S6CMJo32Hy+XfH6yJ2OO5XVmVN6AEFnAjgjqYLsswKKW6xivllR53ZOqYfhnSIvI1Ww/JoUIW+wIIvZ+ud5cMifFFXBEysEe2XHhyf/3+Z9/tkEVrGfYU+XUGBHVwJ4I6mC49JT7gl6uTh6WUVfgD1hT21AWtf/cs6ZRTX8qkErlfLydbB8BaQ1Iac/GpAwLKPP/5vxVSVOrsKc+mLx6n/BIuRVAH00VHR0mW4Yewk/vqjOWXqZRfBk01vZ8wqot+nymYAFpqUxsMSWkoNiZa7rh0tDaRUSksrZIn3lrh6DYDM5SRqQMI6mDFtQbOvIqpfokHTL8kqGuRqSP9JZhb84pkJ0t9AQRJDSnZc6CkzYM6pVuHNLnqrEH6/e/W7JMvFu9qs8d34/RLwI0I6mC5YSkFDi2/VE8qfE36Cpm6lj8x6mMoY5pLCSaAIKl9cb6fv0kJMdKlXWqbPv5Zk3vLiH7t9PvPvrda9ueXt+kZ3JKpY1AK3IqgDpbghgXkxsXjCpm61mXrVAkmJUwAgrElYEhKplb235bU4/384pH6z311ke/v/10mXuOVPoReBUOmDiCogzVkG3vqHFp+aSy9VOO0Va8FWuaEkV3Et1NWXeXesKPA7CMBsIFNhqCuXxsNSWkoNzNJfnbBMP3+91sPy/tzN5tyFidRE7O9/kQdPXVwLZ5VwhLcsIDcmKkjSxeanIwkGdonV7/PwBQALc3U9WnDfrqGpo7qKlNG+Ic+vfLJetmWV2TaeZxADZ8xZkTTkuNNPQ9gFoI6WK6n7nCRM4M6hqSE70mRz7yVe8RjvEQLAA2UV9bI7gOlpmfqfH42fZj+O0/9/Hr09WVS4/GXDyL0oC4jJb7NS2sBqyCogyVkpTt/pUHA4nGCupBNGtZZL10tKq2WlZsOmn0kABYfkuJrv01KiNV3XppFZZJuu3ikfn/73mJ59ZP1pp7JztTvAZ+MVLJ0cC+COlguU6fq4yur/Uu6nYLyy/BQAfHYQR30+19RggmgGZt3FwWsMrBCJmfkgPZy1uRe+v13526WNVsOmXomuyosMWTq6KeDixHUwRJUY7NvAIZTh6VQfhmZKZjfrd7ryIsAAMJjs3HpuMmll0Zqd51vtYLKJKppmKpUFC1jnJjNkBS4GUEdLCEmJjrgCpsT1xpQfhk+YwZ1kOTEWO12ZbVXFn2/z+wjAbCozYYhKX0Nuy7NlhgfK3dcOkrPHB4oqJDn3ltj9rFsRQ2Z+XD+Nv1+u6wkU88DmImgDpaRnebsCZiUX4ZPQlyMTBzaSb8/dxmLyAEcTWW+9hwstWSmTunfPUsu/sEA/f7sxTvl29V5pp7JLtTF3/uf/067sKfEx0bJyWO7mX0swDQEdbDksBTnZ+po5m6taYYpmEvX75fiMn+zPAAoW/b4++lSEs0fktKYi07uJ/27+4PNJ95aKQUO/B0YTqrk/o8vLJRDR6Zlq/aNC4/PseTXF2grBHWw5LAUJ/bUGTN1qUn1pYMI3dC+7STryNJ6b22dfLOKq9sAmu6nU/vpoozN2xZqP7jj0tESHxej3VcXqP751gqp843sRIDa2jptDYTxa3vG6Ezp19n/HAJwI4I6WHIBudMzdZRftl5MdJRMGelf4ssicgDNDkkxcen4saiBKdecM1i/v3jtfvl80U5Tz2RVr3yyTr5dvVe//8PJPWVc//qBM4CbEdTBMrKPZF0UJ5aeBGbqKL8M9xTM77celgMF5aaeB4CFh6RYrJ+uoTMm9pRRA9vr91+YuVbyS5jsazR70Q55e84m/f7oge3lp2cNMvVMgFUQ1MGSmboCw94Zpyir8Pd8kakLj37dMqVzrr+H4uvlDEwB4K+OyDtUFvDzwspUaeitF42QtOT63w9qAMg73+Zr5eUQWb3lkDz59kr9fo+OafLrK8Zo5asACOpg0Z66w0ean53C662Viqr6CV0KQV34ngRNNQxMoQQTgM+WPYUBa2Q6ZCeL1eVkJMlNPxqh3999qFq+WVcibqcmmD7w4iLxeOsD3My0BLnnmgmSnMjvUsCHoA6WzNSVlFdLjadWnKKsMrCEhj114WMM6rbvLZYde4tNPQ8Aa/bTWXFISmMmD+8s00b7f659tapYlqzbL26lng+o1QW+Fob42Gi5++px0t4GQTrQlgjqYBm+SYZO3FVXaii9VM8rkhKYfhnOAQPGXpm5y8nWAVD9dP51Bn0stHQ8GDecP0xyM+svdKrqywdfXiLzXFheri7uPvDSooAy2tsvHSUDemSbei7AigjqYBlqnLMxg+WkYSnGyZeqXCQ62h5XjO24s06VYKqR1wDczZip69ctS+xE/S781WWjtYXaiuqr++trS+Sz77aLW6iVDv96e6Ws2XJYf9nlZwyU44f7px4D8COog6VkZxjXGlQ5dPE4pZfhNmVEF/HFyQcKKmT9jnyzjwTARKXl1bL3cJltM3XKgB5ZctXJ7SQpof6pmlpbpxaTv/PlZnGDGV9ultmL/WsdThrTTS46ub+pZwKsjKAOlpKdZpyAWenIdQYMSYnMkJ1hfdvp979iYArgalsMpZdqmqQdhqQ0pktOvFx9SjvJTve3J7z44ffy6ifrHL2cfMGqPHn5o7X6/cG9c+TmC4fbpi8SMANBHSwly/CLy0kLyMnURd7UUf6SnPkr8sTjdc6gHQAts8mwn66PjYakNKZ9Rpw8+H+TpWOOPzD93+yN8uy7qx1Zar5pV4E88voy/X6nnBS586qxEhcbY+q5AKsjqINl1xoUOKj8srScTF2kTRzaWeJio/Vpacs3HDD7SAAssHTc6vvpgqEyjQ/ddLx075imv+zDb7bJP/63XFuZ4xQHCyrkj/9eKNU1Xv335T3XjpeM1MBBagCORlAHy641cFSmrpJMXaSpX/5jB3XQ789d5r5JcQAaX2fgBGqH3YP/d3xAkDpnyS556D+Lpcbj34NqVxVVHvnjC99JQUn9Bd2Y6CgtQ9e1vT+QBdA0gjpYCj11CNcUzO++36s9SbCb/BKPbNxT4air70BbUpn6/fnljgvqlPSUePnTjZNkaJ9c/WXfrdkn9z+/0JY/73y06Z6vLpFtef49oz+bPlyG9/P3SgNoHkEdLNtT56iVBobySzJ1kTN6YAdJSazfAVhV7ZWF3+8Tu1BDDz6cv02e+HCfvD73sDzx9iqzjwTYPkungqB2WUniJGotzr3XTQioTFix6aDc88wCbeqnHb0483tZvNa/YP38aX3ltAk9TD0TYDcEdbBsT11hSZV29c4JSg3ll2TqIrvrcNKwzgE76+ygssojj7y2TJ7/4Htt0bDy5dLdsmIjfYFAa/rp+tp8SEpTEuJi5K6fjJMTRvoHRK3fUSB3/usb21W5fLJgm7z/9Rb9/oQhHeWqHw4y9UyAHRHUwbI9derJbVGpM4alMP2y7Uw1lGAu23DA8t9DeQdL5ZePfy1zlx8dgD7z7mqp8VCGCYQc1DlgSEpTYmOi5Y5LR8sZE3vqL9u+t1h++8R8OWAoP7Uy9TP66XdXB+wT/MWlo7V+OgAtQ1AHS0lKiJWkhBjHDUth+mXbGdInV8/4qnHf81fmiVV9t2av3P7YXNmxr0R/WZcc//fH7gOlMnOe/wo2gJYOSbHf0vGWUMHPz6YPk+kn9tVflneoTH7z5HzZfcD/c8WKdu4rlof/s1hfy6B+bv/+p+MlMaG+hB5AyxDUwXKyjMNSHBLUGadfEtRF/kmOsSTJiiWYqqz4Px+vlT+/uEjKK/3DDc6b2lt++oP2MqZfiv6y/87aIIeLKkw6KWAvKjN/oMD//6Vv1yxxOlVe+pOzBsuVZx6nv+xQYYX89sn5snWPfwm71b5O9/97of7zLyE+Rn5/zXhtwieA0BDUwXKyM4xrDaxdOhcsyi/NK8Fctz0/YBKeFZ7M3Pfst/LWF5v0l6ns9G+vGis/+eEgLSg9aViGpCXXf59UVnvlhQ++N/HEgH1s2e0PYjJTEyQ30//7xOkuPLm/3HjBMP1+UWm13PWv+bJ222GxErWDTl3Q8v1cVi2Pv7xstKOmlAJmIKiD5ThtrUFVjTegL4pMXeT16ZIhXdql6ve/bqRfzQwbdxbIbX+fq02q8+nWIVUe+flUmWwY8JKcEC1XnuG/6v71ij2yarP/fQAcu59O9Wc5cUhKc344uZfccekoiT7Sk1ZW6ZF7nv1W612zypTfx/+3QrvY5nP1WYNlwpBOpp4LcAKCOliO0xaQG7N0SmpyvGlncQv1RG7aaH+27qtlu7UnE2ZRj/3pt9vlN0/M18qifCYP7yx/u/UE6dbh6OW6J4/tFrBk+Ol3VouH3XVAs9wyJKU5J47upi3tVoNUfOtd/vjvhbJglfn9xW/M2hAwFEqtLThvah9TzwQ4BUEdLCfbYbvqjHuD1C/Z+Fj+27UFY1/dzn0l2lQ4szK16sr0k2+v1IMydRX9mnMGy2+uGKPtnGqMehtVSuVLNOzaXyIfzt/alkcHbGdTwJAUdwZ1isp83XftBEmMrx88pn72qKEksxftNO1Mqr/59Vkb9PvD++Ue+RnnrmwqECk8u4SlM3UFDuipK6vwBPTT8QusbXTOTZX+3TNNHZiy73CZ/Pqf82T24p0BfT5/unGSnDe17zG/F/p3z5JTx/sX8L7+2QZHZK+BSFC7TY2ZcGOm242G92+n/azx9XGrIZP/+N9y+cCEibrrtuVrj+2jyuN/e6U/mwig9fjfBEv31OU7oKeOyZfWGJgyd/kefXR2W1iybr/c/ve5AdPnjuuZLY/dMVWG9skN+uNcccZx+tCUiiqPvDiToSnAsUovM9MS9NUmbjagR7Y88H+Ttc+Hz3PvrZE3Pt/QZiXp6uLWn19aqPeWpyXHy73XTqAVAQgzgjpYTlaD8ksze6HCXX7J5Mu2NWV4F/HtsFVX8NtiCpwKHNUagvv//Z2UGvopzzq+l/z5Z5NbPLI7IzVBC+yM/YFrthwK65kBJ9hi7KfrmklVxBG9OmfIwzcfL+2z/D97Xvt0vbww8/uI/35VPeVqdYGaxKmozNzvrh4nnXL9a1sAhAdBHSzHeHXV462T4jJ/UGT3QSlk6tq+lHd4v3YB2bpIB/B/fGGhvP7ZevE9V1L7l35x2Wi54fxhEhdiP+WpE3pqk/x8nnl3tXgZmgI02U/n9tLLxsrRH755inRt758K/N7cLfLPN1doezMjwXukj0/1A/vc+uMRMrh3TkQeD3A7gjpYjgp8jE9+C0rs3VdXaii/JFNnbgnmNyv3BKyXCCdVZnn7Y3O1sksfdTVaTbecZjhDKNTuOuP+KTX05aMF21r1MQGnZ+oQKDczSR666Xjp3cV/gejzRTvlr68uCfvPRZUBfOa91bJ8o38Vy49P6a9N5gQQGQR1sBxVMuOktQal5WTqzDRxaCd94mhJeY0sj8C+pjlLdsqvHv9a9h32LzkfP7ijPHrbVOnZKT0sjzGwR7b8YFz3gPIpJ+xxBMJBleofKvL/fzBmthFYzv3AzybLoF7Z+su+WZknf35xoVRW+4d6tdbMeVvlkwXb9fvHD+8sl542MGwfH8DRCOpgSdlpzllrYCy/TD0y8AJtR60MGDu4Y0SmYNZ4vPKvGSvl7/9dLtVHrnSrHr4rzzxO7vrJuLBnZq/64SD9wkB5pUde+nBtWD8+4IQhKWotTkt7V91E/Qz5w/UTZdTA9vrLlq4/IPc9991Re1VDsXjtPvn3B2v0+wO6Z8ltl/gXogOIDII6WJKTMnUB0y+b2EmGyDKWP373/T4pN3xNQqUGr9z55DcBV6PVVLf7rpsoF57cPyJPYLShKaf7r3bPWbJLGxUOuN3m3f4ps327Zpl6FjtIjI+Vu68eL5OHddZf9v3Ww/K7p7+RotLQWx625RVp5Zy+Nr12WUnyu5+Ok4S4+n15ACKHoA6WlGPcVWf3njrKL003emB7/XNfXeOVhd/va9XHW7npoNz2969kw84C/WV9u2Vq6wpGDvBf/Y6E0yf1kt6d/aVlT7+zKmKDDgC72BywdJzSy2Co3vVfXTEmoKx7y+4i+e2T8+VwkX/fX7DUBdj7n/9OKqq82v2khFi555oJkmVYUwQgcgjqYElOzdRRfmmOuNgYrafDuBYg1Ob/GXM2yT3PLNBHdCunTeghD9+kRoYnS6Q1HJqyNa9IPmVoClzOWH6pLrAg+J8nt1w0Qs49oY/+st0HSuXXT8yXvEOlQX8c1Y+nJv/6+hpVocJvrhwTtp5iAMdGUAdLUj0RTumpC8jUUX5pmqkj/SWYKzYelMIWlhipks0HX14sL320Vi8tUle6b71ohNx84QiJb8PyouN6ZctJY/xT5F75dH2rSqYAO1MX/owX/5h82fLhZNecMzhgkMmB/HL57RPztUm7wezm/Pt/lwVkS687b6iMHtghYmcGcDSCOlg+U1dQbO8nq+ypswa1GyknI1F/EqImvgVrx75iueOxufLt6r36y9pnJ8tfbpkiPxjfQ8zwk7MGSUpirP499vJHDE2BOxmzdOr/uPH3B4IP7C45dYBcd+6QgNaHO5+cLxsNZeaNefXTdbJglf9n41mTe8lZx/eO6HkBHI2gDpZfQJ5fUqmVvdmRCh6MQzkovzSPGlxygiFb9/WK4IK6ecv3yC//8bXsOVimv2zUgPby99ummpoRUH0qlxqGpqh9U+t3MDQFbu+nI0vXGuec0Ed+/uMRWvmkUlpRI3c//Y2s2uzfN2c0e9FOeeuLTQH9y9caAkMAbYegDpZkbKyuqvZKRVX49ue0JXVu4wwLyi+tMwVzw44CyS9t+vvK462V595fLX95dYlUVtc3/isX/2CA3HPtBElPiRez/XBSr4CeFYamwI02GYM6+ula7ZRxPeTXV46V2Jj6yE4NPlHrDhau8WfjlDVbDsuTb6/Q7/fomCa/vmKMxMTw1BIwA//zYEnqCbNq4Lb7sJSGO38ovzRXr87p0q1Dqn5/zXb/snAj9f32u6e+kQ++3hrwtfv9NePlstMHBnxvmkk9ebrh/KEBk+tmfedfsQA4nari2GIckkKmLizUqoPf/3SC3itc46mVB15eLHOX79HuHy6ukYdfWSIeb/1FpMy0BG3SpdoLCsAcBHWwbKmc+iVh+6DOUHqZlBAjsVzBNL1vZKohW7dqe/lRpb1qV9Ntj34law3731QwqMotxw3yLzG3iiF9cgMykP/5eB1DU+Aa6neDce0NQV34qOXkf7xhoiQf6d1V7QSPvbFcvllbIq/NPSwlR4aAxcdGy91Xj9P6jAGYh2eYsMlaA3s+SWXypbWnYB4q9si+wvqvkQruPvh6i5ahMz5JPHF0V20gSqfcFLGqq88erO2E8vXAvPLJOrOPBLR5P11uZlLAxUC03qBeOfLAzyZLRmp9ubm6Bvb5iiLJL/GXrt92ySgZ0CPbxFMCUAjqYFnZhr46u641UE+wfVKTze/BgkjHnBQZ2CNLv796e7nW+/i3V5fKc++v0XvSVD/Jz6YPk9svGSWJ8fUBk5UHC1162gD9/qyFO445sQ5wgk0BpZcsHY+EPl0z5cH/O15yj0wPNrr89IEyZUQXU84FIBBBHSwrK90B5ZcV/gXV9NNZR0AJ5rZy+c0T38jXK+p7RXxj0R+86Xg5c1IvrWTTDtQI8e4d0/Sr6c+8u0orlwKcTPWR+jAkJXK6dUiTh2+eIp1y/CWW00Z1kYtO6W/quQD4EdTBsnIcsKuutMJfokL5pXUcP7yL1replFbWys79JfrrhvXNlcdunyYDbVZOpPo1bzx/mH5/485Cbc0B4FSqZNpYftmvqz8Dj/BTPXMP3jRZJgxIlWlD0+WmHw2zzUUvwA0I6mCPBeQlds3UsaPOilTfzYh+uUe9fPqJfeX+6yfati9naN9cOcFQCqUWkpeU+7PFgJMcKqyUQsNQoD6UX0ZcZmqCnD46Uwvq4mLrJ2MCsAaCOlhWwAJym5ZfllJ+aVknjemm31ZDRn571Vj5yVmDbb9j6afnqKEp9U+2VEDH0BQ41WZDP137rCTJSLXnxRgACAd7P3uBa3rq7DooxZipo/zSWiYP6ySnjMiQ0X1T5G+3HK/tZXKCnIwkbUG6z6ffbg948gs4hfH7Wg3zAAA3I6iDLTJ1ZZUeqarxit2UGXrqKL+0FtULcvygNDl7XJZ0ae9fSO4EZ0/pI12P/JvU0JSn32FoCpwd1PVjSAoAlyOog6Vr94092HbM1gWUX5KpQxuJiw0cmrJhR4HMWcLQFDh3SAqZOgBuR1AHy1K9TRkphhJMw0JoW5Zf0lOHNjS8fzuZPNxfUvrSR2ullKEpcIiDhRVSXOb/fu5LUAfA5QjqYGl231XH9EuY6Zqzh0hCfP3QlKLSannt0/VmHwkIC2OWTo3aT0+JN/U8AGA2gjrYZ62BDXfVlRqDOjJ1aGPtspLkx4blwB8v2CZb9/iXNQOO6KcjSwcABHWwtuw0++6q83hrpbLaP9yF8kuY4bypfaRLuxTtdu2RoSmqHwlwSqauL0NSAICgDnYqv6yybemlQqYOZlALgq8/zz80Zd32fPly6S5TzwS0ekiKIVPXl6XjAEBQB2vLMZZfltg3qIuOEkmMjzX1PHCvUQPby8ShnfT7L3649qiLDoBdHCiokJJy//cvQ1IAgKAOtuqpq7RtP50qvYxWkR1gkmvPGSLxcfVDUwpLquT1WQxNgf1LLzvmJEtqMkNSAICgDrZZQG63TF3DoA4wk5oQeNHJ/fT7H87fJtv3Fpt6JiAUgaWXZOkAQCGog20ydWonkcdrnwEP7KiD1Zw/ra90yjkyNKW2jqEpsP+QFII6ANAQ1MHSstL8g1KUskr/NElb7agjqIMFqPLL688fqt//futhmbt8j6lnAlo1JIXJlwCgIaiD5Z+EGgOikopasQvKL2FFY47rIOMHd9TvvzhzjZRXMjQF9rA/vzzgZ2sfMnUAoCGog61KMEttmqlLSSSog3Vce+4QiY+N1leF/HfWBrOPBARlk6H0slNuClUQAHAEQR0sL9uwq66kwp5BHdPZYCUdc1LkRyf5h6bMnLdVdu5jaAqsb4uh9LIfWToA0BHUwVaZOjsFdYHll+yog7VccFI/6ZCdrN321tbJM++uZmgKbJWpo/QSAPwI6mB52WmG8ksb9dQFDkohUwdrSVBDU87zD01ZtfmQzF+RZ+qZgOaoiw4BmTqGpACAjqAOlmffTF21fptBKbCicYM7aoNTfP49c41UVHlMPRPQlL2Hy6Ss0v/92btLhqnnAQArIaiD5eUYB6XYKKhjpQHsQGXr4o4MTTlcVCn/+5yhKbD+frou7VK4WAYABgR1sLws46AUW02/9F9RJqiDVakJghec2Fe//97cLbJrf4mpZwIas3l3kX67b9csU88CAFZDUAfLyzZk6soqa6W2ts4WvR+UX8Iu1CTM9llJ+tCUZxmaAotn6vp2o/QSAIwI6mCrnjr1PLOsyvrDUqpqvOLx+p8UE9TByhLjY7XddT4rNh2UBav2mnomwEhdzNuyxxDUMfkSAAIQ1MHykhJiJSkhxlZ9dcZ+OoXyS1jdhCGdZNSA9vr95z9YI5UMTYGFhqSUHxmSEhXFkBQAaIigDraQlWavCZjGHXVqCEV8nD8oBawoKipKrj9/qMTGRGn3DxVWyJtfbDT7WEAjQ1JSJTmRC2UAYERQB9uVYJZWWr/8ksmXsCP1ZPn8af6hKe9+tUXyDpaaeiZA2WzYT0fpJQAcjaAOthuWUlJur0wd/XSwk4tO7i+5mfVDUzzeWnn+g+8ZmgLTbQoYkkJQBwANEdTBFuy21sCYqSOog50kJsTKtef4h6Ys23BQ1u+uNPVMcDc1JGUrQ1IAoFkEdbCFbENPnd0GpVB+CbuZNKyTjOjXTr//6bJCqfZYv+wZzrTnYKlUVNX/3GdICgA0jqAOtuupK6mw/pNLyi/hpKEpRWVemb+WheQwxxZDP13X9mnaRGQAQCCCOthCTrp9M3UEdbCjbh3S5NwT+uj3v1lboo2VB9rapoAhKWTpAKAxBHWwXU9daaXX8oMbKL+EE/z4BwMkJ6P+goq3VuTFD9eafSS40JbdRfpthqQAQOMI6mC76ZfqyWVJeeBybyuXXxLUwa5UmdvVPxyk31/0/X7ZsbfY1DPBXby1dQHllwxJAYDGEdTBFlQJo1ri7VNQYu1pfJRfwikmD+8kHbP838Mzvtxk6nngLnsOlEhldX3JfbQaktKZ8ksAaAxBHWwzuCErzV+CWVBcJfbJ1MWbehagtf/3Jh+Xpt//evkeOVBQbuqZ4B6bDaWXXTukaSs3AABHI6iDLSdgHiyoEPtMv+RJCOxtUPckyUyJ0cvh3p+7xewjwSU2U3oJAEEhqINtdG2fqt+etzJP7DMohUwd7C0mOkomGbJ1ny3cIcVl1aaeCe6weZc/qOvHkBQAaBJBHWzj5DHd9NurNh+SXfutuTertrZOyivpqYOzjOidLOkp9Rcoqqq98vGCbWYfCQ6nssJb8wyTL8nUAUCTCOpgG8f1zJL2mf4A6dNvt4sVlVd5xLhxgaAOThAfGy0/nNxTvz9z3laprPaYeiY42+4DJdoFBCU6Okp6dk43+0gAYFkEdbDVwIax/VL0+18s3imVVR5Ll14qKYn01MEZzpjYUxLi63vrVPnlF4t2mn0kuKT0srsakhLPz1IAaApBHWxlWM9kiY+N0m6XVXrk6xV7xGpKy6sD9nzFxPDfDM6gyi9PG99Dv//O3C3iVYsjgQgHdZReAkDzeLYJW0mIi5bhvZL1+x99s03qjLWOFlBGPx0c7NypfbTBKcqB/HKZb/GhRXDK5Ev20wFAcwjqYDtj+vmnYG7dUyQbdxaIdSdfEtTBWdpnJcsJI7sELCO32oUV2J/KAG/NK9bv92XyJQA0i6AOttMhM04G98rW73+8wFoDU0rLydTB2aaf2E+/vS2vWJZvOGjqeeA8uw6USnVN/ZAUlRnu2ZlMHQA0h6AOth3Y4DNvxR4pKq0SK5ZfkqmDE/XolC5jjusQkK0DwmnzLn8FRveOaZIQVz+gBwBg86Bu27Zt8stf/lJOPPFEGTZsmJx66qny97//XcrKylr8sb766iu59tprZcKECTJkyBCZPHmy3HrrrbJq1aom32fBggVy9dVXy8SJE2XkyJEyffp0eeuttyg7Msn4IR0lMy1Bu13jqdUmYVpFqaH8kkwdnGr6iX0D9kZuMjwJB1pr82720wGA44I6FWxdcMEFMnPmTGnXrp1MmzZNysvL5emnn5aLL75YSkqCX0L96KOPyg033CDz58+XLl26yNSpUyU9PV0+++wzueSSS+S999476n1ee+01LaBbvHixDBo0SMaPHy9btmyRu+++W37729+G+V+LYMTFRgdM4fvk2+3a0m8rKDOUX5Kpg1MN7p0jA3pk6fdnzNls6nng4MmX9NMBgP2DupqaGrntttu0IO6hhx6SN998Ux5//HGZPXu2nHTSSbJx40Z55JFHgvpYS5YskWeeeUaSk5Pl1VdflRkzZsiTTz4pn3zyifzhD38Qj8cj99xzj+zbt09/n61bt8qf/vQnLfB7++235d///rcWTH788cfSvXt3LQhUt9H2TpvQU44M4ZN9h8tl+cYDYgWllF/CJXsjjb11C1bnSd7BUlPPBGfwaENSyNQBgKOCuo8++kj27NmjlUief/75+ssTExPlgQce0AI0FWwVF/unZDVFvZ2iSi/HjBkT8DqV8VNZu6qqKi1r5/Pcc89JbW2tXHPNNTJw4ED95Z07d9YCQOWFF14Iy78VLdMuK0nGDuqo3//4G2sMTGFQCtxi/OCO0rV9/TRaVYn+zldk69B6u/aXaGX1+pCUTulmHwkALM/yQd2XX36p/a166BrKysrSSiFVNk+VUx6LCgT79++vvU9jevfurf194MCBgP67ph5/0qRJWgZv9erVcujQoRb8qxAuZ07upd9evG6f7M8vFyutNCCog5NFR0fJBdP8vXVzluySguJKU88E+9tkKL1UQ3niGZICAMcUKxanyiuVAQMGNPr6fv36aYHfhg0b5Mwzz2z2Y913333Nvn7lypXa3506ddL+VoFafn6+JCQkSK9e/uDBJyYmRgsEV6xYoT1+bm6uhIsawOL11o9zNoPxsc08x7HONLR3tnTKSZa9h8u1TMEnC7bJFWcMNPVMxqAuOSGmzT9/dvnamY0zhedMU0Z0klc/XSf5xVVaduX9rzfLFWccZ+qZzGLFc9nxTMahO326pLfJue34eTIDZwoOZwoOZwpOsEMZLR/U7d+/X/u7Qwf/+GwjNTilYXYtFHPmzJFly5ZJXFycnHLKKQGPrR5D9Y809/gHD4Z3T1NFRYUWLFqBykRajfFMQ7vHyd7D9bc/WbBVjmtfIbExUaadqaDYP5E1b88OWVHj79E060xWwpmcd6bRvRPl8xX1a0U+nL9V+uVWSGJctGs/T1Y9l13OtGpD/e9eJaGupM1/F9rl82Q2zhQczhQczuSC8ksV3PhKJxvje7kapBIqlWW788479X67jh07Bjx2UlJSk++rsnhKKKsVEB4jeqdI7JHqnPKqWlm7q/7rZpbKav8VlcS4tg8ugbY2ul+KJBz5Xq+qqZOlm/l5iNB4vHWyv9Bf7dA5J97U8wCAXVg+U6dKHNWgkmMJdV+cWpdw/fXXS2FhobYD75ZbbtFfFx0dfMwb7n11KpBsquS0LaiUs+8KxdChQ7Wvg9maO9PSHTHyxZJd2u11eSJXnjfClDPV1kVJzeu79dePHjlU2mU2fVGgLc5k9a8dZ3LGmc46tE5mfLlFu710S6Vcf+EkifNdbTHpTG3Niuey25m27ikSb+0e7baquPjBCaMj9n0U7JnMwpk4E2fiTL7kky/RZOugLiUlRQu41FTKxlRW1jflqymYLfXpp59qe+bUJ0oNQlGrEYxfPPXYxsdojO9coTx+c1S5pxW+kRR1DqucpakzqYEpelC3PV927i+VXp0z2vxM5eWegJelpySY+rmzw9fOCjhT68907gl95YN527S+OtVfN2/FXvmBYZekGWcykxXPZYczbc3zT7JWUy8TE9o+U2eHz5MVcKbgcKbgcKamNdUCZrvyy/bt2zfbs+brpfO9XbDUfjq1/04FdJdffrn84x//kPj4wF8evj6+5iZbhvr4CK/+3bMCFtR+ssCc9QZlhh11ajJgUoLlr5sAYZGVnignjemm35/x5WaprQ1vBQOcb/Nu/366PuynA4CgWT6o85Ugbtq0qdHXb95cvxcp2FJFVcqpsnNqgbkqr/zd734nv//97xsttczMzNQCOxX47dpVnwVqmKJVy8kVtSoB5vrhpJ767S+X7pJyQ4DVVkrLq/XbKYlxQV9dAZxArTfwfcvvOVgqC783b0gQ7GmzYfJlP8OFOgCAzYO6adOmaX/PmjXrqNcVFBTIwoULtWElEydODOrj3X333fLuu+9qPWsqW3fllVeG/PjffPONlJSUyODBg8nUWcDxI7pI6pG9cJXVXvnySDlmWyqr8Jdf+s4CuEXndqkycWj9Shhlxpebwt5vDOeq8Xhl+15/+SWZOgBwUFCn1gt06dJFWwL+xhtv6C9XfW4qy6amXl500UWSnZ2tv04tI9+yZYv2R932ee+992TGjBlafexTTz2lDUY5lksvvVRiY2O1t1dDVXzy8vLkj3/8o3b7xhtvDOO/GKFKjI+VU8Z11+9/tGB7mz+hDFg8nkxQB/eZfmI//faGHQWydlu+qeeBfezYW6JNv1RiY6KlR8d0s48EALZh+YYftbLg4Ycf1lYN3HvvvfLmm29K165dZfny5Vo/25AhQ+T2228PeB+1X863iPyLL77Q3l6VSj722GPay9SScBXcqT+NmTJlipx77rna7YEDB2of/69//atccsklMm7cOC0zqDKEKqC8+OKLtSErsIYzJvaU9+bWT+Dbtb9Evt96WIb0Cd9S+GMprfCXX6YmEtTBnf2tw/rmyqrN9b3Ib8/ZJIN755h9LNjA5t2F+u1endMlLtby150BwDIsH9QpY8eOlbfeekueeOIJWbRokdZHpwI1laG7+uqr9SmVxxoHunfvXj3omzlzZpNvm5WVpQd1igooe/XqJS+99JKsXLlS65Pq06ePXHbZZQFvB2uUf43o305WbKwfrPPxgu1tHNQZMnWUX8LF2TpfULdk3X7ZsbdYenQi64Lgg7q+lF4CgPOCOt8gEjXcJBgq4FNBnNGgQYOOellLnHzyydofWN+Zk3rpQd2CVXlSUFypTeZr6/LLVMov4VIjB7ST3p0zZGtekd5bd8elo80+Fixu0y5DUMeQFABoEWob4DjjBnWQ3Iz6IM5bWyezFu4wJ1NH+SVcSlUzXHBiX/3+18v3yIGCclPPBGurrvHKzn3+ISlk6gCgZQjq4DgxMdFy+kT/eoNPv90uXm9t2w9KofwSLnb88M7SPjtZv7jy/pFeV6Axauqlb0iK6qXr3jHN7CMBgK0Q1MGRTh3fQ2Ki6xdmHSqqlEVr97fJ41J+Cfgvrpw/tY9+/7OFO6S4zD9ICDDa0mBIipp+CQAIHj814Uiqh27SsM76/U8WbGuTx6X8EvBTK0bSU+K121XVXvm4jf4fwub9dJReAkCLEdTBsc6c5C/BXL7xoOQdLI34Y5KpAwJ3R551fG/9/sx5W6Wy2mPqmWBNW3bXD9VRCOoAoOUI6uBYajeWsS/jk2+3R/wxWWkABPrh5F6SEB+j3Vbll18s2mn2kWAxVTVe2WEcksLkSwBoMYI6OHoC35mGgSmzF+2MaJagrq4ucFAK5ZeAVn552vge+v135m5ps8FFsIfteUXaMB0lXg1J6cCQFABoKYI6ONqJY7pJ4pEsgcqizV+xJ6JXm31PTBTKL4F6557QR6KPDC46kF8u81fmmX0kWMhmQ+llry4Z2pAdAEDL8JMTjpacGCcnju6m3/9oQeRKMEvL/Vk6hUwdUE+tNjhhZBf9/jtfbtYy24Cy2TAkpR/9dAAQEoI6ON4ZhoEp6snDxp0FEXmcskp/UKdKiOLj6jOEAESmn9hPv701r0gbXgQomw3rDPoQ1AFASAjq4Hi9OmfIoF7Z+v1PIpStK6vw9+tRegkE6tkpXcYc10G/P2POJlPPA2tQZes795fo9/sxJAUAQkJQB1c4Y1Iv/fbXy3dLSXn4lyAz+RJo3vQT++q3V20+JJt2RSZrDvvYnlcstb4hKXEx0rV9qtlHAgBbIqiDK0we1kkyUuuXIFd7auWLxeEfq87kS+DYa0YG9MjS78+Ys9nU88BipZcMSQGAkPHTE64QFxsjpxrGqn+8YLt+dTgSPXWpyfUBJIDANSPGbN2C1XmSd7DU1DPBOpMv2U8HAKEjqINrnD6hp0TVT1WXvYfKZMWm8A5qKDNMvyRTBzRu/OBO0qVdfYmdGoD5zldk69xsyx5DUNc1w9SzAICdEdTBVWPVxx7XUb//yYJtEczUEdQBjVH76i4wZOvmLNklBcWVpp4J5lCl8LsNQ1L6MvkSAEJGUAdXOXOyf73Bou/3ycGCirB9bAalAME5cXRXyU5P1G7XeGpl5vytZh8JJthXUCO+KvjE+Bjp0j7N7CMBgG0R1MFVRvZvLx1zkrXb6snEZ9+Fb70Bg1KA4Htczz2ht37/42+2Sbkh0w13yMv3TyHurYakRB+pjwcAtBhBHVxX+nXGRH+27rOFO7RMQTiwpw4I3ukTe0pyYqx2u6zSI59+u0OcwBvmAUxOtjffH8hTegkArUNQB9c5eWx3iYut/9YvLKmS71bvDcvHpfwSCF5yYlzABZb3v94iNR6v2JX6//+XV5fKA2/ukY+X+Mf0I7hMHZMvAaB1COrgOhmpCTJlRBf9/kdhGpgSMCiF8kvgmM45oY/EHtlLll9cKV8t3S12tHNfsfzisbmyYNVe8daKLNpYKrsMA0BwtKqaWjlY5K9uIFMHAK1DUAdXOnOSP0Pw/dbDsmNvcXh76ii/BI5JDUs5eWw3/f6MLzeHfX9kpH27Ok9++fjXkneoLODlX6/IM+1MdhmS4pOUEKOvuQAAhIagDq7Uv3uW9DHsRPrk29YNTFFPRMsrDT11lF8CQTl/Wl99f+Seg6WyaO0+sUvv3KufrJMHXlosFVVHl43OW7FH6tQiPgQxJCVT63cGAISOoA6uFBUVJWdO6hWwK6s10/eqPIFP3uipA4KjMjQTh3bS78+Ys8nywZDqn/vTCwvlf7M36i9TQ19+dsFQ/f6+w+WyaRe9dU1hSAoAhBdBHVzrhBFdJOXI9L2KKo/MXRZ6P09lde1RQyAABGf6if302+t3FMjabfliVTv2Fcsdj82VJev26y/r2j5VHvn5CXLahB7So328/vLW/ExxOoakAEB4EdTBtRITYrVJmD4fL9gecoagwhDUqSv27FsCWlYOPbRPrn7/7TmbxIoWrMqTXz3+tew19M+NH9xRC+i6HlmcPbRn/R5M5esVe1hx0IiKSo8cKjYOSfGXwgMAQkNQB1c7wzAwZfve4pAzBMZMHf10QMtNP6mvfltlwcIxvChcVGD2yifr5MGXA/vnLj1toNz1k3EBmflB3ZIk+shvVrUyZfXmg2Yc2dK25hXpt5MSYqVzLkNSAKC1COrgaurq+vB+/gzBxyGuNzAGdfTTAS03akB76dU5Xb8/48tNluqfe7NB/9zvrxkvl5w64KgBH8kJMdK3U6J+f+6yPW16XjvYvNsf1PXuks6QFAAIA4I6uJ5xYIoqr1JX11uqssZfYkVQB4Q2vOgCQ2/d18v3yIGCcsv2z40b1LHJ9xvaw1+CuWB1nlTX2HepeiRs2e0fIMOQFAAID4I6uJ7qiVH7shSPt04+X7SjxR+D8kug9aYM7yzts5P1ksf3v95i6f65pgzomiiJ8THabbXqxBgUup3qW96w0xjU0U8HAOFAUAfXi4mJltMn9gzYWdfS4QbGQSlk6oDQ/y+eP7WPfn/WdzukpNw/JbEtqP/7//l4bVD9c02Jj43WAkCfucuZgumj+pb35/szsAN7ZJt6HgBwCoI6QEROHd9d7+s4WFAhS1t4ZZ2eOiA8ThnbXdKS69cCVFZ75aNvQutzDbV/7o///k7e+mJTUP1zzZkyorN+e/Ha/VJWEfoeTCeZvWinfrtXhwRpl5Vk6nkAwCkI6gARyclIkolD/AuQP2rhwBRjT11qkn9PFYCWrxo5+3h/n+vMeVulsto//j7S/XNL1x9oUf9cU0b0b6cHpzWeWvluzV5xu/LKGpm/0j84ZmSfFFPPAwBOQlAHHHHmZH8J5rL1BwJ6aVqWqatfaA4gND88vrckHOlJKy6rli8M2Z1I+GZVnvzyH4H9cxOGBNc/15TYmGg5frg/W8cicpF5K/K07KuSGBclx3UlSwcA4UJQBxyhlh+rK/M+n367PcRBKWTqgNZIT4mXU8f30O+/M3eLeL3+/2Ph7p976OXFerARFSVy2ekD5c6rguufa87UUV312ys3HZSCkkpxs9mGIVRqSXtcLKsMACBcCOoAw0h143oDNQWzKshR5MZBKUy/BFrvvBP66D1sB/LLtWxaW/TP3f3T8XLxD1rWP9eU43pmS25mfTZKzV6avyK8/wY72bW/RNbvKNDvU3oJAOFFUAcYnDSmm172VVJeI98Y+j+aU1nDoBQgnNRqgxNGdtHvz5izWRuHH6n+uW4dUuXR26aG1D/XFBUYTjX8G9w8BXPWQn+WTi2Z75xNRQMAhBNBHWCgArJphpKpj78JrgSzsto4KIWgDgiHC6b11W9vzSuS5RsPRqx/7m+3niBd2vnLr8PFWIK5YUeB7DscfK+uU6hBMV8u3aXfP2VsN1PPAwBORFAHNGAswdyws0A27/Ivym2MWliu/viQqQPCo1fnDBk9sL1+f8Ycf6lkuPrnLg9T/1xTenZKl24d0lydrVuybp8UldbvG4yLjQ7IwAIAwoOgDmigd5cMGdgjS7//8THWGxiHpCgEdUD4TD+pn3571eZDsmmXvy8rWKXl1Y3vn/vpePlxmPrnmuvVnTrKUIK5bHfYykjtYtZC//RStTrGt+oBABA+BHVAI86c7M/WzV2+RxuqEEw/XUx0lCQe6ckD0HpDeufIgO5ZAb11LbFjr+qf+7rR/rmxYeyfa87Ukf4SzF37S2X73mJxi8NFFbJs/X79/g/Gdzf1PADgVAR1QCMmD+usjVVXqmu8MmfxzqAmX6osnboyDyA81P+n6Sf5e+sWrM4Leoek1j/3+Ney19DHNnFop4j1zzWlY06KDDBk/920s+6Lxbu0yZ++4TfD+rYz+0gA4EgEdUAj4uNi5AfjugeUYDZVMmUckkLpJRB+4wd3ki7t6kfgq/+G783dElr/3BkD5bdXjo1Y/1xzjH1kX6/YI7W+SMfB1M/M2YbF8aeM7R7RUlcAcDOCOqAJp0/sqT0RVPYcLJNVmw4FsXicoA4INxUInD/N31s3Z+luKanwBt0/l+Lrnzslsv1zzZkyvIv4HvpgQYWs254vTrdm62E9S6p+lp7M1EsAiBiCOqCZkqnRAzvo9z9qYmAKO+qAyDtpTFfJTk/QR+Qv3FAaZP9cWpv2zzUlKz1RhvVr56opmJ8bdtON6NdO2mclm3oeAHAygjqgGWdO6qnfXvj9Pq3pvyEydUDkxcXGyDlT+uj3F28qDbig8s3KpvrnpkjnNuyfC3ZgyvwVeeLxBk7OdZKyihr5ZtVe/f4Pxvcw9TwA4HQEdUAzRg3soDX3K6oH5rPv/FeemxqUAiByJdFqFYFSVVMnSzeX+fvn/mOd/rmmqCBT7WlTSsqrZUUYlqlbleobVEOmlLTkOG3BOwAgcgjqgGaoFQVnTPRn6z77bvtRV9eNg1LI1AGRoy6aGP8/fre+RP784qKj+ufuuWaCqf1zzZ1/7KAOrpiCaSy9nDa6m5ZpBQBEDkEdcAxqCmZsTP1/lfziKlm4Zl/A6+mpA9rOOSf00f8/llTUyrINB4/qnxtznD9wshpjCeZ3a/ZKZZVHnEbt4du0q1C/b5wkDACIDII64BgyUhPk+BGdA9YbGNFTB7Sd7PREOXG0PzCyav9cU1TA6SshVeWii9YGXiRygs8X+bN0fbtlSq/OGaaeBwDcgKAOCMKZE3vpt1dtPiS79pc0GtSRqQMi77ypvfV1I+rvK844Tu68ylr9c83twJw01H+RaO6yPeIkNR6vfLnEX1Z6Klk6AGgTBHVAEAb2zJJendMbzdYF9tTFt/nZALfp0i5VfjwlR4b3SpY/XDdBLjqlv0T5ojwbmDrKv4h86fr9UlxWLU6hpgSrITBKfGy0TDGUmwIAIoegDgiCesJ45iR/tm7Okl16L0xFQE9dfVkVgMga2DVJzp+YLcP65ordDO3bTjLT6nfuqemdC1bliVN8vnCnfnvS8M6UpANAGyGoA4I0dVRXvRemvNKjLQ+uq6uj/BJAi6fqThnhz9Z9vdwZJZgHCypk+Ub/4vdTx7GbDgDaCkEdEKSkhFg5aUw3/f7H32yXyiqv1PmrLym/BBCUqSP9Qd2arYfkUGGF2N0XS3bqPw875aTIkD45Zh8JAFyDoA5oAWMJ5ta8Ilm6wX9VWiFTByAY/btnScecZO22CoTmrbB3tq62tk4+X+QvvTx5XDdb9TkCgN0R1AEtoPZgGXt43p6zWb+dEBcjcbH8lwJwbCrgMe6sU+XcdrZ6yyE5kF+u3VY7308Zy9RLAGhLPAMFWuiMST0Dluz6MCQFQEv7dH227C4KWJVi5wEpowZ2kJyMJFPPAwBuQ1AHtNCEIZ0k68jkOiNKLwG0NPPf27CY264DU0rLq2XBav8Ez1PYTQcAbY6gDmih2JhoOW2CP1vnw+huAK3ZWeebqGs3c5ftlhpP/RTgjNR4GTeoo9lHAgDXIagDQnDahB4SrRpHDFISCeoAtMyUEf4SzL2HymTTrkKxm88X+0svTxzdjd5iADABP3mBEORmJsn4wYFXoym/BNBS7bKSZHDvHNsOTNm6p0jrB/Sh9BIAzEFQB4Toh4b1BgpBHYDWDkyZt3yPeGvtU4L5+cId+u0B3bOkR8d0U88DAG5FUAeEaFi/XOmcm6Lfp6cOQCgmD+ssMUfKuQtKqmTNlkNiB9U1XvlqmT+z+IPxZOkAwCwEdUAr9kxdfvoA7XZMtBxVjgkAwUhPiZeRA9oHDB6xg+/W7JXSihrtdkJ8jEwZ4R/6AgBoWwR1QCtMGtZZbj2no/z8nE7Sp6t/NDkAhFqCuWBVntR4vGKn3XTHD+8syQyLAgDTENQBrZSdGivpyTFmHwOAjalMv8p2KWWVHlmy7oBY2f78clmx6aB+/wfjeph6HgBwO4I6AABMlpQQG1DCbfUpmF8Y1hh0aZcig3plm3oeAHA7gjoAACxWgrn4+31SXlnfr2Y1ajrnbENQd8q4HlqPMQDAPAR1AABYwMj+7SUtub4vrdpTqw0isaKVmw7KwYIK7XZ0dJScNKab2UcCANcjqAMAwALiYqNl8nD/BMm5y/aI1XfTjRnYQbLTE009DwCAoA4AAMuYOtIf1KlBJAUllWIlxWXV8t2affp9dtMBgDUQ1AEAYBGDeuVIbkZ95qu2tk6+WZkn/9/efYBJVZ1/HH+X3QWXoog0KYoNBGkiJaI0KcaWqLHFWLEXjF2Uvxqxa9QExRiNvcRYsWAialTQRFAEUaqCSBUUEKQvu/N/fgfvMLvMrDM7M7eQ7+d59mF2dnbnZebOuec95z3nhsl7n863TWXl7nb9erWsa9smQYcEACCpAwAgPLRGrde+LUJ5IfJYLFbh2nT9u7a0okK6EQAQBrTGAACEtARzxjcr7NtlaywMvlrwg81dvCr+ff9ulF4CQFiQ1AEAECK7N9/BWjSuG/9+3ORwbJjy1oQts3RtWzWwlk3qBRoPAGALkjoAAEJE13xLvGZdGEowN5SW2diEOAaxQQoAhApJHQAAIdM7oQTzm29/rFD2GIT/TFlka9ZvcrdLahVWuPQCACB4JHUAAIRMs4Z1rfUu9UMzW/d2QunlgZ2aW0mtokDjAQBURFIHAEAI9UnYBXPspAXuEgdBWPz9Gpvy1ffx7wf12DWQOAAAqZHUAQAQQgd2bm41CjbfXrpinc34Znkgcbz98ZZZupZN6lqbXXcMJA4AQGokdQAAhFCD7bezjns2CrQEs6w8Zu8kJHUDu+/qNnIBAIQLSR0AACHVp8uWDUk++GyRbSor9/X5J81castWrne3C2sUWL/9Wvr6/ACA9JDUAQAQUr/o0MyKCjefqlet2WiTZ33n6/O/NeGb+O3u+zS1+vVq+fr8AID0kNQBABBSdUuKrVu7JhU2TPHLytUbbMLUb+PfD+zOtekAIKxI6gAAiMgumB99sdjWb9x8vbh8e3fifNtUtnnHzQbb17IubRr78rwAgMyR1AEAEGJd2zWJXxdu3YYy+3jakrw/ZywWszHjt2yQ0r/bLlb4UxkoACB8aKEBAAixWsWFtn+HnX3dBXPWvBU2f8mP8e8HUHoJAKFGUgcAQMj16bKlBHPijCW2eu3GvD7fWxO2zNK132Mna9awbl6fDwCQHZI6AABCrtOeDa1+3c07T2qd24dTFuftudZv2GRjJy2Mf88GKQAQfiR1AACEnNazHdi5mS+7YH44ZZGt27B5M5ba2xVZz45bnhcAEE4kdQAARKwE8/PZ39uylevyXnrZe98Wtl3NzZu0AADCi6QOAIAIaLPLjtakQW13OxYzGzd5S4lkriz8brVNnbMs/j2llwAQDSR1AABEQEFBQYXZunzsgvl2wizdrk3r2V4t6+f8OQAAuUdSBwBARPTet3n89lcLVrqZtVwpKyu3f3+yJakb2GNXl0gCAMKPpA4AgIjYten21mrn7ePfj83hbN3EGUtt+aoN7nZRYYH1TZgVBACEG0kdAAARUqEEc9ICi2mBXQ6MGf9N/HaP9jvbDj9dQgEAEH4kdQAAREjvzltKMBd+t8ZmL1iZ9d9csWq9fTx9Sfx7NkgBgGghqQMAIEIaN6ht7XZrUGG2LlvvTpxv5eWbZ/wa1i+xzq0bZ/03AQD+IakDACDCJZhjJy20sp8SsupQ+eaY8Vs2SOnfraUV1mCDFACIEpI6AAAi5oCOzeKJ1/JV623qnO+r/bdmzF1RYRfNAd0ovQSAqCGpAwAgYrSJyb5ttpRIvv9p9S9E/taELRukdNyzoTXdqU7W8QEA/EVSBwBABPVJuGbdh1MWWemmsoz/xtr1pTZu8sIK16YDAEQPSR0AABGkyw7ULC50t9esK3XXmcvUB58tsvUbNyeDdUqKbf8OO+c8TgBA/pHUAQAQQSW1iqzHPk3j379fjQuRvz1hywYputh4rZ+SRABAtJDUAQCwDZRgTpi2xJVTpmv+kh9t+tzl8e8HcG06AIgskjoAACKqy95NrG5Jsbu9sbTMxk/9Nu3ffSthlm73ZjvYni3q5yVGAED+kdQBABBRxUU17IBOzTIuwdxUVm7vfjI//v3AHszSAUCUkdQBABBhffbdciHySbO+s5WrN/zs73w8bYn98NPjlBgmXswcABA9JHUAAERYu913sp122M7dLi+PuR0tM7k23f7td7Z6tWvmNUYAQH6R1AEAEGGFNQqsV+fmaZdgLlu5ziZOXxL/ntJLAIg+kjoAACIusXxSO1ouWb425WP//cl8K49tvt14xxLruGcjP0IEAOQRSR0AABG3R/MdrHmjuvHvx05KPlsXi8UqXJtuQLddrEaNAl9iBADkD0kdAAARV1BQUGG2buykhUkfN3XOMlv0/ZqffsesP9emA4BtAkkdAADb2IXI5y5e5b6qujZd570aWeMda/sWHwAgf0jqAADYBjRrVNf2bFk/ZQnm2vWlFXbGHNhjV1/jAwDkD0kdAADb4DXr3p+00K2h84ybvMg2lpa52/VqF9sv2jcNJEYAQO6R1AEAsI3o1bmZWysnS5evtZnzfoj/7O2P58dv992vpRUXFQYRIgAgD0jqAADYRuy0Q4l12KPhVhumLPmh1L6cvyXBG8gGKQCwTSGpAwBgG5K4C+aHny2ysvKYTZq9ecdL0bq73ZrtEFB0AIB8IKkDAGAb0rPDzlZUuPn0vnLNRvtq0XqbMnfLxciZpQOAbQ9JHQAA25C6tWta17aN49+P/uQHW7uh3N2uWVTDeidspgIA2DaQ1AEAsA2XYK5au3nHS+nZqZnVLSkOKCoAQL4UWUR8/fXXNnLkSJs4caItW7bMmjZtaocccoidffbZVqdOnWr/3RUrVtgRRxxhHTt2tPvvvz/pY7Ql9IsvvmjPPfecffnll7Zp0yZr0aKFDRo0yM466yyrW7duFv8zAAByq1u7plZSq9DWbdiS0Mmg7lybDgC2RZGYqZsyZYodffTR9tprr1mjRo2sb9++tnbtWnvggQfshBNOsB9//LFaf1d/Y8iQIfbdd99V+bihQ4fasGHDbNq0adapUyc74IADbPny5e75jznmGHcbAICwqFVcaPt3aFbhviYNats+u+8UWEwAgP/hpK60tNQuvvhil4DddtttbrZsxIgR9vbbb9tBBx1ks2bNsrvuuivjvzt//nw75ZRT7OOPP67yce+//76NGjXKJZOvvvqqPfbYYy6Ze+utt6xLly5uBvHee+/N4n8IAEDu9d63eYXvB3RraTVq/HQROwDANiX0Sd3o0aNt4cKFbnbsqKOOit+/3Xbb2S233GK1a9e2F154wVatWpXW31u/fr09+OCDduSRR9rnn39uLVu2rPLx48aNc/9qRm733XeP37/99tvbBRdc4G5PmDChmv87AADyo/Nejaxh/e3cbV1n/KCubJACANuq0Cd17777rvtX69cq23HHHa1Hjx5uNu+DDz5I6++98cYbbmavqKjIbr75Zjv//POrfHyNGptfoiVLlmz1M6/ssn79+mk9NwAAfiksrGHDTutuPVrXtRP7NHQXJgcAbJtCv1GKyiulTZs2SX++1157ucRv5syZduihh/7s31MCpkTu9NNPd7NtL730UpWP79Onjz3++OP28ssv22677ebW9mmW8KOPPnLloEr6Bg8ebLmmzVnKyioucPdT4nMHGUciYkoPMaWHmNJDTNGOa5cmdeyQrvVDFVMYXydiSg8xpYeY0kNM6ecE6SiIpfvIgGjd2po1a1zi1qxZxUXf8sQTT7gZNyVbt956a8Z/X0nd1Vdfbf3790+5+6XKNbVubuPGjRXuV+mmnluzhbkyffp0t34QAAAAAERLztq2bWuRLb9ct26d+1ezY8l49+czEerdu7d1797dPVe3bt2sV69ervRTm6089NBD9sMPP+TtuQEAAAAg0uWXhYWFVl5e/rOPy9eEo3a/vPDCC61Vq1b2+uuvxzdW0ezhdddd5+7Tteq0K2dBQe52FSspKUlZcuoHTTlrIxnp0KGDex+CRkzEREzEFNaYwhoXMRETMRETMUU7Ji0x8ya5Ip3U6cLimgnbsGFDyt0svSnJXNNFxm+44QZXdnnnnXdW2ClTcWn3TV1DT19jx4516+9yRQliGA4kURxhicVDTOkhpvQQU3qIKdpxEVN6iCk9xJQeYkoPMaWW7qRR6MsvGzdu7P5NdYHwpUuXVnhcLs2bN89dTkF/e++9997q57Vq1bL999/f3fayegAAAADwU+iTOq8E8csvv0z686+++qrC43LJu/ZdcXFxysd4GbwuqwAAAAAAfgt9Ute3b1/375gxY7b62YoVK2z8+PEVZsxySevolLRptm7GjBlJyzN1aQNp165dzp8fAAAAACKf1A0YMMCaN29u7733nj377LMV1tINGzbM7Xp53HHHWYMGDeI/06zZ7Nmz3Vc2M2i6pt1hhx3mbl955ZW2ePHi+M+0xm/48OE2Z84c22WXXaxfv37Vfh4AAAAAqK7Qb5SiywjcfvvtduaZZ9r111/vdpls0aKFTZo0ya2na9++vV1yySUVfmfJkiXxC5G/88477vHVpR0u586d6zZDGThwoLukgWYGv/jiC7fOr2HDhu4adjVr1sz6/woAAAAA21xSJ0qknn/+ebvvvvtswoQJbh2dEjXN0J1++uluJ8p8qVevnj399NP2zDPPuMsXTJ482c3+afZQs3hKNhs1apS35wcAAACAyCd10rp1axsxYkRaj1XCp2s6pOPoo492X1XRLNxpp53mvgAAAAAgTEK/pg4AAAAAkBpJHQAAAABEGEkdAAAAAEQYSR0AAAAARBhJHQAAAABEGEkdAAAAAEQYSR0AAAAARBhJHQAAAABEGEkdAAAAAEQYSR0AAAAARBhJHQAAAABEGEkdAAAAAEQYSR0AAAAARBhJHQAAAABEGEkdAAAAAEQYSR0AAAAARBhJHQAAAABEGEkdAAAAAEQYSR0AAAAARBhJHQAAAABEWEEsFosFHQS2mDx5spWVlVlBQYGVlJQEFocOi3Xr1rnbikPxBI2YiImYiCmsMYU1LmIiJmIiJmKKdkyKR3EVFhZa586dUz6OpC5kPv30U/fGAQAAAIAowezSpYulUpTyJwhEcXGxlZaWWo0aNaxWrVpBhwMAAAAgIBs2bLDy8nKXI1SFmToAAAAAiDA2SgEAAACACCOpAwAAAIAII6kDAAAAgAgjqQMAAACACCOpAwAAAIAII6kDAAAAgAgjqQMAAACACCOpAwAAAIAII6kDAAAAgAgjqQMAAACACCOpAwAAAIAII6kDAAAAgAgjqQMAAACACCOpAwAAAIAII6kDAAAAgAgrCjoAhMtHH31kp556asqf165d2yZNmmR+W758uT344IP27rvv2qJFi2y77bazjh072tlnn209evTwNZaDDjrIFi5c+LOP6969uz355JPmt7feesueeOIJmzZtmm3YsMGaNm1qffr0sfPOO88aNmzoezz//Oc/7ZlnnnHxxGIxa9Wqlf3mN7+xY4891mrWrOlbHHPnzrUjjzzSPe+wYcOSPuY///mPPfTQQzZjxgxbv3697b777nbCCSfYMcccYwUFBYHFlcnj8h3PunXr7NFHH7U333zTvvnmGysvL7cWLVrYgAED7Mwzz7Ttt9/e95h+/PFH++tf/+qOfX0269SpYx06dLCTTjrJ+vbtm/N40ompstWrV7vHz58/39555x33mvkd04gRI2zkyJEpf1+vlV5HP2OSmTNnuvZ9/Pjx9sMPP1j9+vXtgAMOsAsvvNBatmyZ03iqimnBggXWv3//tP6GYhsyZIgvcXlKS0td2/7KK6+4z56oPT388MPdeTsf7enPxbRx40Z7/PHH7bXXXnMxqY/Qrl07O/nkk3P62dP/+YUXXnBts9qgnXbayfbff3/XB1A7Xdkbb7zhXqs5c+ZYWVmZ7b333nbKKafYwQcfHFhMif7yl7/Yn/70Jxs1apS1bds2kJjUr/rb3/7m+lVqN2vUqGG77rqrHXbYYe54qlWrlu8xLV261O6//34bO3asu63zSdeuXe20006zLl265CSebN+7xYsX269//WtbuXKla7vChKQOFUydOtX9qw6RThaV5epDnonZs2e7Bua7776z5s2buwRFJ98PPvjAPvzwQ7vvvvtch9Ivei41hsmog/uvf/3LnUTat29vfvM6bUpA1ACqczRlyhR76qmnXFxKrtRo++X666+3Z5991t1u06aNe//UiA4fPtyddNV477DDDnmP4/vvv7fzzz/fNd6pPP300y6u4uJiN1CgfzXI8X//93/2ySef2O233x5IXJk8Lt/xqMOtztqsWbPcyXbfffe1wsJC+/zzz10yoPdUr2OTJk18i0nJ0oknnuhi0on5wAMPtDVr1rgEfdy4ce4kfdlll+UsnnRiSuYPf/iDS+jyJZ2YvPa9X79+Vrdu3a1+rs643zFp0OeKK65wCcs+++xjnTp1cgNA6uy+9957ruOVy8SuqpiUkBxxxBEpf1ftvs45al/9fq30+uhY1nGtOLt16+YGyT799FP74x//aP/+97/tsccey+k5Op3P3umnn+7OMWov1W/QgMpnn31m55xzjmsrlAhmMyCm/+Pll19ur7/+unsOnVcbNGjgziMvv/yyO68pQVKH3HPHHXfYww8/7F4nteVKPCdMmGAXXXSR+//8/ve/r3Y81Y0p0ZgxY1y/JZcyjUlt0e9+9ztbsmSJazd/8YtfuNdJ791dd93lBu2UrCdrJ/IV07fffusGD5TMNWvWzPX1li1b5mLRa3bddde5tt7P1ylZH0/tlRK6UIoBCS655JJY69atY2PHjo2FQWlpaeyII45wMd1yyy2xTZs2xX/2/PPPu/v322+/2IYNG2JhcPfdd7uYTj311Aqx+mHWrFmxNm3axDp37hybOHFi/P7169fHhgwZ4uI666yzfItn1KhR7jnbt28fGzNmTPz+jRs3xm688Ub3s8svvzzvcUybNi02cOBA93z6uummm7Z6zOzZs2N77713rGvXrrHp06fH71+4cGFswIAB7vdGjx7te1yZPM6PeK655hr3s5NPPjm2fPny+P0rV66MnXHGGe5n55xzTiAx6XnXrl0bv3/q1KmxLl26uJ999tlnvsaU6rPgfc2fPz9n8WQS0wEHHBBr27ZthdcpX9KJSa+D2qt99tmnwudLbcSwYcPc7w0ePNjXmFJRe37SSSe53xsxYkTOYko3rqeeesr97JBDDoktXbo0fv+3334bGzRokPvZQw89FMhnr3///rGZM2fG71+2bFnsxBNPdD977rnnsorD++wceOCBFZ5D74d3vu3Zs2dszZo17v4PP/zQ3devXz/XfnvUrvfo0cP9bPLkyb7GlOjRRx91x7v3uup1zoVMY1IfRfepz5cYp46no446yv1s+PDhgcR07bXXujbAo/5ou3bt3Oum+PyMqbKRI0dWaMvDhjV1SDqSG8QsUzIqp9L0tkYlr776ajcr4FFJXK9evdyMgUZ2g6YRXM1WaNRHI12JsfpBM5cahdJMYmKZgkZuL7nkEndbo5V++fvf/+7+Pffcc23gwIHx+zU6NnToUNtjjz3s1VdfzVv5gkbS7rzzTjvuuONcWVBV5W4qudQI3BlnnOHKdDwaLdTooDzyyCO+xpVJ/H7Eo3JUjW7KbbfdZjvuuGP8Z/oMaiZTI/Lvv/9+1qOYmcSk2UE97w033GAlJSXxn2kmxZt1USlPtqr7fmhEXDPAmoXSzEEuZRKTRr9V7aDPXeLrlGuZxKTZlLVr19pZZ51lhx566FZthErHNXqvMnK/Ykrl3nvvde2nyuovuOCCrOKpTlyadRbNjDVq1Ch+v2bFBw8enLP2Pd2YNBuuEjavPWjdunX8ZzoH6m8UFRW5EsNNmzZVOx7N1Ipm2xOfQ+fXiy++2Pbaay83o6gZTHnggQfcvzrnqf32qF3X43PRlmcak6iaQeWft956q9WrV8/NaOZSJjGp0um///2vi0NtU2K7pONJVQWiklq/YlJJoypj9LqoOkZtgEf9vJ49e7rZasXtV0yVTZ482VVCqQ0IK5I6VCilUCOuErnEDluQVJojWquTjOrBVXbSuXNnC5LKFtSpVFJ17bXXunIGv6keXtQJqswrF1U5pl+8ZC3ZGhWd7JWoi5KAfNB6Ch0f6mConELrQlJRmZcMGjRoq5/pZKKkRSdlNfZ+xZVJ/H7EozIYJSYaMEjsLHl0zKuUVslxtq9TujFpba06uyrVS1byqVgksYOQ75gSqTOrUh+VY6tELtcDPZnE5NeAXSYxqX1XW6BEpTKVfaltGD16dNYlhdl+lrQEQL+vZPiWW26Jt7XZyiQu79hRuVw+2/d0Y9JaNXWy9Zxa81SZ2ohddtnFtQXesVcdans1ELHffvtt9TMN5uy2227xQQv1YVQqr8+71r5XpvZdv6NBHq9tyHdMHpV8as2o1hmqzC/X5+JMYlJbrtJ5rVtNVl7prSlTgq+BMz9i2nnnnV3C9txzzyVdG+q9X2ovsrF9Nd470bGltlx94xtvvNHCijV1iJs+fbpLSrTmSmuddMKdN2+e+9CrY6tadO+A98sXX3zh/lXSpvU8GpVX7bM+2DqRaNGz3zNiqUaclRDrdUoccfaT1hKps6HRWnU8tLBYDZBGl7yRN42I+0UdWdFoYDJe46zOQT5olP+qq65yNfjq/KfqWKjToU6ROo7Jjm8dXzrJ6XVUoprtZjPpxpXu47KV7vNosEdrMlPR8a/PqF6vxo0b+xKTqH1KnF31aPG/ZhL0vubiM1md90PrZnTcaMDn5xbf5zsm72fq1GjgSaPiGgDS31A7qjVQqT6r+YhJswUrVqxwo+WKScePzjma2dTggDrlyZKFfMaUys033+wSGM3Q5XJ9XyZxaX3R22+/7TaU0QCGKjL0WdN9qhDR72sNm18xeZ3sqmacvDZeSbHWSlZHVRv76BzjxaekQM+j+/QeJYtLiarab81Yq2+TbN+AXMfk0Zo1VQ6kWquVrUxi0nvhrXVPRmskRYmnjgE/YhL1VypPKOg4e+mll9zMmd6/bDffGVmN907UhqttUlWP4ggrkjrEeQezPjwTJ050Myk6sHW/yuR08lBpg1+7TWr2SzsyqVOmGDRdrk6ARxsyaOZAMWXbicyGdt7TyKZceumlgcWh0SeVdqicQguc9eVRQ6nyoWQzUfmMR2WxSjKPOuqoCj/zFviLRg3zQQuu0+GNfKukKdWCfq/cSZ0Bv+JK93HZytXzaCZKNPqbbXJQ3ZiUKOgz8OWXX7okQe2Xvs9FRzzTmDRjoM62NiXRDqr5kElMXvuuzTTUKdFIvTrwGjhTR0Wl7tqtN9u2NN2YvN0blaCo/bznnnsqlOlpoEyzRDfddFPWM63ZHONKflVar5loDZTlUiZxabmBjmtteqXyNH15dB7U65SLzVvSjUkJkZJK7Uatz13lMk21619//XX8dj5okEl9BJ3flDR5JapVbdSktlztuL6qm9RlEpNHA61BSRVTqsRGn0U55JBDAotJiaUmFzTZoMGnPffc051jstm4pboxqQxVfWANmvTu3dtWrVplYUX5JbY66au8Sltu66TqXUZAuyRp7YNqjjUN7QfveTRSo+2jNRr/4osvumRAo0zaaUsxawYxm1KKXKwdU6waQVJMQVJJgTqRGiFVp0231UlTMqyOWz5336tMly3wdiLzRv5E75V26fTWQSp5D5K3s1tV64y88i+tI8HWNLCi3ck0qqsSlaBoB0wNPnkJgwSx5bRO+tohTclTkJ25RN7n7be//a0ra1SHSUmcdnvTjJi2rte6ZT8Hw0S77amzprh0DH388ceufdCMispqtT45SN6Andbb5nMt4s9RFYYG5ZTAedUzmvXRbXV8dZmRbNceZkKzqZot9HYTTCwL1XurY0mzm/lq41Wqp3OLaMBX7436KOm25d5j8x1T0DKJSe+lZqR0vlbbpb5VUDGpXVDf01tOon6D2vd8+W+KmNRnUqWTkkq16WHHTB0qlJjoQ6yRrMTRENU3a1tiJVM6eWjEItttZdPhnQh0YtCsj06uXjmHEhadxH75y1+6tU5KQhM34/CLRrW8a9HlqwFMl0bctWBejdHzzz8fH7XV63f33Xe7xeG6NITWqPhxstExojUE6qgdf/zxLuFVR02dbHUANHuh5DzbGvlsZbI+Ric9VPTnP//ZJQh6HZXA6NIVQQ5qqMpAbYdG7bWBg2bqNKjhbRbkB5U3agZDg2JhKdXR516j0Cp3TJyR1qyGkiqNymuzJZWwqb3NNy8BUQKstiJx5knloIpLbYRmprSmOohrbH711VfuOFIZmh/nvKqoTdemTaqgUZvqrdvWLJg6oTov67jX59HPS9aoPde1a3Uu1rVj1V/wLnGg+zRokIs1rYnU2dcAs/6/el+82cVMlmLkeiA4VUxByiQmzZKr3VKpowbnNLCSjwqodGPStfI0O632Qce7+jBXXnml+71cv7bvpohJr4kSObVVGlwK4pJemWKmDnFqjLWmKNn0thpLr5ZZSZQfEhMPzRRW7vyrxOtXv/qVu53tjkjVpdJCLajVKE511wzkispvtLBZJ/7EMhydUNUYqsOrTp0abT+ok68OhuJRR1IDAupwa6RZHRSvjDcfF6vOhLf2oqoF4V4HNNe7F0aZToA6rpTQ6RjTbnc6EQdJswdqv5RI6eKwKjlWAqMBDa3384N2V1NHVhc+1zqosNDrooQ7WYmxylS9NsOv9j3xs6TBpsq0jlpthQalVMoaBG+3V63JDHLWRceuBifUqVTnMnEjLt1Wh1fnQx13KtH0i55bbblmMVWypvZdz6+LoSvJ9DYDycVaTY8GUbW2Ue21yuG83YmDbMuriikomcSkxElratU30GuosnFvI7OgYlIbrs+cBnf0WC0rEe2mmsuE/MkqYtL6Ow1YaFlNsnXbYcRMHdLmLRzN9wWQEzshSjTVeUy1rbJ3f6qLgeebTqLiJZdBUYOkDRmUfGvDlMrUkVMHUyddb/MZvxI7JeT6qkxreCRfW/Wny1t/UdWOjd5OWEGu3QwTzQ7oRKgTnj6nGtXVWrqwUVmh1tNpUwR1NvPRUUlW8eAdM5VLUb3Opjro6rBoJipXm4Hkqn3PR1laMokzmFW172qvgmrfNUMQhvZdibZKv1Whkng5g8TXUoOKmmnVkgRty+4XDcppcEdflWnWN1dtvGZN1LH/xz/+4c5nmp3UxdiTteVVrX3OZVueTkx+yzQmlaorodP6R70mSuhysTYzm5iSUSWBKsZ0nlZJZrIdmHMZk55Hr4UmE/SZSmzLE9f+evefd955vlQ4/BySOjhKnDTTo86aDvRkW/LrOiLJdgXKFyUoOjnpA5VsG+fEjngQlxBI3I4/qB0vE9cwqDRQjVOqEhTvfm+dQ76pI60vzQ4k64h4s6sq2QmSRpPVGdAxpvr5yptqqMTW26Ez8bo2/6v0nmrDCM36akdMnfj87EQm0vuiDYE0upysUyne9tjZXCsrE15S5CUEyWjdn2hdlB9JncoItUZagyxe0hl0++7NGqrd0mdPx1KY2ne1BUpK1HlUMhUkb2OGqsoY/W7fRedmJUkaMKxcxq4kVOuidL9mXLOhwRANIilpVWmgromp0s7KVDGjTrjeO83IVS6X0+CA+jgaUNHlFvyIyU+ZxqQyWe2IrZngtm3burXR2jwpiJg0KK0qB21ek+wSVjq+vWM827Z8fRox6THeDt7ejH0y3rX8VLIZhqSO8kvEOz46wNXZ0Pq0ZEmfLicg2v3HL17Jp0o5KlNnwLuocBAXg9TJTB0hJSy53Oa6OtTpUXKixi7Vdd+0g5vkehQuFW1qo7Ic72KfiVSKqVkexRyGGR7vOEvWEdfrpqRZHZP/9Zk6db51AV0ldFojqdKroBI60Ule6zKV2FW+rpCXgGoEWh09dVr8oDVGqb68MjS1sfr+6KOP9iUmdVxUWqXPojZEqUz3qVOlkjQ/ZjNFM7xeQpusfddsizZ30XuX7JpS+abXw9s4LGheZ1ExJZu1VPukBEr8Os5FA8Hnnnuua8sr07GmfoMGLrJZV6qOtdcB199RuVyqREVJnHYt1PNqnVRlb775pus3qA+TzaWQMonJL5nGpPZH52cldErKtfNjrhO6TGLSYJjOJ9o7IVn5rJa66DEq8002AJTrmFq0aJGyHddmTh7vPr92hf85JHWI8xaCq2Zf14Lz6AN2zTXXuGl6JU/5us5KMipPUnmHLrOgUSRvowr9q5Ivlebounra5dFv3o6OQe94KRoN1e5xopnWxF2i1Ijpell6DbXmqPLlBfJFO6NpJF5bqCfuuqlEWKUOeg9V9hGGdWo69tV51MV2E3fq1GYX3oVG1Xn5X6dF43r/NGOpRCqoGXKPPvsaFNBgxtChQyvszKtt1rX4Xce/RlFzfbHfKFEHxVvfp9cpMTFQKdNFF13kXiddBDyfW4ZX5l03U7O96mR59D7qnKNOnNZnBbFJire2MAztu9bzKLFVsqINf7R2OnEWT7PU6pwrSc73xeUTeZfIUZ8hsWxXHXCtfVL7r89gNtQm69jQeUIXRv+5yg4NOok2SUrcBVd9Gm8TmWxLJDONyQ+ZxKTjSO+Ljh0t19C66HychzOJSUmRruWpTa20ri1xx1S9d97OvEpEs0nI/xLC9y6XKL9EnEqqNOKm2TptR6+SE42KaNdLlcHoA6cF2X7SzIhOGEOGDHHXTtFoszqUSlrUYKujpp3bvBIrP6nTKMlKC4Og0Sc1fhqh1CYRGmFWEqf7NLOiRkwntcoX98wXdYZURqFLKeiiq94MgHbEVGmMkstcX/cpm06TOkva7EPJsQYvNOqrWNVZ0eCCn9f4CyPNWOr1EA20aOe7VLSwPNs1D+nSjpta4K74+vfv7zbY0Hum5FwDUr169fJ1q/6w0qyKXie18TqWvZJCdcD1OmnHSb938FWiqbZdG9qos6Z1YRo916yTEk99LpXcBSFs7bvOvUpYdN08VRaoPVUiroFNJXQqW/P7/KzjSdUNWqutY0rvn943b+ZOA4zZJMVKXlU2LN56r1R0ztNnXceUBuk086Tzjmbu9Dqp7VJpqgYUs0l8qxNTvmUakwbmvCUFGhDWQE8qGtSsziZB1Xmd1MdTn+CVV15xyzN07Oh40jGu906VDclKM/MZU9SQ1CFOMxWa0VHZhL5UL6/GUKWF6uhqu/wgZlVUKqHyHM3UabbpvffecyO3xx13nJvpCWqjDe9C6EHv3ujReguNQinx1ZeSOSVParz0WmlUPNt1BJnSCVTHj67lp86I1j7pxK/jSQufU13sOwg6WWj3V80sqlOp2FT2pE1e1MD/r0ssZ/q53QjVQfcrqVPJkI53DR6og6nkToM8KjNWJ0ADVJlctmJbpXZAJdEqb9LrpM+j2gy9TprJ1CBLEJ9HXYNUs1D63Km8UO2Wyqv0uQvqnCPebGYud27MxXGu10llhN4Ai9p07bTq9yyr12fQ8aTzjpZnqPRf52YleF6Sng2v5M4rEU5WOuxRouZ1wjXTo+913tHf0ACdBnv0GmngJ4iY8inTmLxjR7wlLKnotaxOUled10mDOOrrKdnS+Uax6bk1gKE+Q7YDqxNC+N7lWkGMCy8BAAAAQGQxfAkAAAAAEUZSBwAAAAARRlIHAAAAABFGUgcAAAAAEUZSBwAAAAARRlIHAAAAABFGUgcAAAAAEUZSBwAAAAARRlIHAAAAABFGUgcAQIAWLFhgbdq0cV/jx4+v8rGjRo2ytm3buseecMIJtnr1at/iBACEF0kdAAARoITu6quvtvLycuvevbs9/PDDVrdu3aDDAgCEAEkdAAAh98orr8QTup49e9qDDz5oderUCTosAEBIkNQBABBir776qg0dOtQldL1797YHHnjASkpKgg4LABAiJHUAAEQgoTvooINs5MiRVqtWraDDAgCEDEkdAAAh9Nprr7mErqyszA4++GAbMWKE1axZM+iwAAAhRFIHAEDIvP7663bVVVe5hO7www+3e+65x4qLi4MOCwAQUiR1AACEyOjRo+3KK690CZ1m5pTcFRYWBh0WACDESOoAAAiJN954w6644gqX0NWoUcM2btxo11xzjcVisaBDAwCEGEkdAAAh8eyzz7qE7tRTT7Xhw4e7+8aNG2ePPPJI0KEBAEKMpA4AgBA544wz3Ozcsccea/3793f3aU3dlClTgg4NABBSJHUAAITE4MGD3Xo6z0033WQNGza00tJSu+yyy2z16tWBxgcACCeSOgAAQqJv374Vvm/QoIFL7GTevHl2/fXXBxQZACDMSOoAAAixfv362fHHHx+/1MGLL74YdEgAgJAhqQMAIOR0EfJWrVq525q5mzNnTtAhAQBChKQOAICQq127tt1xxx1WVFRka9eutUsvvdRd7gAAACGpAwAgAjp16mTnnnuuuz19+nSX5AEAICR1AABExHnnnWcdO3Z0t5988kl75513gg4JABACBbFYLBZ0EAAAAACA6mGmDgAAAAAijKQOAAAAACKMpA4AAAAAIoykDgAAAAAijKQOAAAAACKMpA4AAAAAIoykDgAAAAAijKQOAAAAACKMpA4AAAAAIoykDgAAAAAijKQOAAAAACKMpA4AAAAAIoykDgAAAAAsuv4f9t4p1A2JfaMAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.lineplot(x=\"K\",y=\"Silhouette Score\",data=score_df)\n",
    "ax.set_xticks(cluster_range);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "1c7d1662-c252-4d72-a6af-6fcb9071f457",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "In the figure above the minimum silhouette score is observed with 15 clusters.  Let's run KMeans with num_clusters = 15.\n"
     ]
    }
   ],
   "source": [
    "opt_num_clusters = score_df.sort_values(\"Silhouette Score\").K.values[0]\n",
    "print(f\"In the figure above the minimum silhouette score is observed with {opt_num_clusters} clusters.  Let's run KMeans with num_clusters = {opt_num_clusters}.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "33842af7",
   "metadata": {},
   "outputs": [],
   "source": [
    "num_clusters = opt_num_clusters\n",
    "km_opt = KMeans(n_clusters=num_clusters, random_state=42, n_init=\"auto\")\n",
    "clusters_opt = km_opt.fit_predict(X)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3bd9f1d",
   "metadata": {},
   "source": [
    "Routine to plot a silhouette plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "2b3a617a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def silhouette_plot(X,cluster_labels):\n",
    "    \"\"\"\n",
    "    Adapted from https://scikit-learn.org/stable/auto_examples/cluster/plot_kmeans_silhouette_analysis.html\n",
    "    \"\"\"\n",
    "    sns.set_style('whitegrid')\n",
    "    sample_df = pd.DataFrame(silhouette_samples(X,cluster_labels),columns=[\"Silhouette\"])\n",
    "    sample_df['Cluster'] = cluster_labels\n",
    "    n_clusters = max(cluster_labels+1)\n",
    "    color_list = [cm.nipy_spectral(float(i) / n_clusters) for i in range(0,n_clusters)]\n",
    "    ax = sns.scatterplot()\n",
    "    ax.set_xlim([-0.1, 1])\n",
    "    ax.set_ylim([0, len(X) + (n_clusters + 1) * 10])\n",
    "    silhouette_avg = silhouette_score(X, cluster_labels)\n",
    "    y_lower = 10\n",
    "    unique_cluster_ids = sorted(sample_df.Cluster.unique())\n",
    "    for i in unique_cluster_ids:\n",
    "        cluster_df = sample_df.query('Cluster == @i')\n",
    "        cluster_size = len(cluster_df)\n",
    "        y_upper = y_lower + cluster_size\n",
    "        ith_cluster_silhouette_values = cluster_df.sort_values(\"Silhouette\").Silhouette.values\n",
    "        color = color_list[i]\n",
    "        ax.fill_betweenx(np.arange(y_lower, y_upper),\n",
    "                    0, ith_cluster_silhouette_values,\n",
    "                    facecolor=color, edgecolor=color, alpha=0.7)\n",
    "        ax.text(-0.05, y_lower + 0.5 * cluster_size, str(i),fontsize=\"small\")\n",
    "        y_lower = y_upper + 10\n",
    "    ax.axvline(silhouette_avg,color=\"red\",ls=\"--\")\n",
    "    ax.set_xlabel(\"Silhouette Score\")\n",
    "    ax.set_ylabel(\"Cluster\")\n",
    "    ax.set(yticklabels=[]) \n",
    "    ax.yaxis.grid(False) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4949b6d2",
   "metadata": {},
   "source": [
    "Plot a sihouette plot for the clustering above.  When we do this, we're looking for two things. \n",
    "1. How uniform are the sizes of the clusters (represeneted by the widths of the bars in the plot below).\n",
    "2. How consistent are the Silhoutte Scores for the inidividual clusters.\n",
    "\n",
    "In the plot below, the red dashed line shows the average silhouette score for the clusters.  Are the peaks for each cluster near the line.  Usually there are one or two outliers. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "8f9f9b96",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "silhouette_plot(X,clusters_opt)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc1a6294",
   "metadata": {},
   "source": [
    "Make a TSNE plot of the clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "98754749",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tsne = TSNE(n_components=2, init='pca',learning_rate='auto')\n",
    "crds = tsne.fit_transform(X,clusters_opt)\n",
    "color_list = [cm.nipy_spectral(float(i) / num_clusters) for i in range(0,num_clusters)]\n",
    "ax = sns.scatterplot(x=crds[:,0],y=crds[:,1],hue=clusters_opt,palette=color_list,legend=True)\n",
    "ax.legend(loc='upper left', bbox_to_anchor=(1.00, 0.75), ncol=1);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5831c201",
   "metadata": {},
   "source": [
    "Add a cluster column to our input dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "1eb019af",
   "metadata": {},
   "outputs": [],
   "source": [
    "opt_cluster_df = df.copy()\n",
    "opt_cluster_df['Cluster'] = clusters_opt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a16bbc0",
   "metadata": {},
   "source": [
    "Display structures of the cluster members"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "f21b7940",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "86ce3554d4664d188cc1c68f2a73ec37",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "MolGridWidget()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<style>\n",
       "    /* Some CSS to integrate with Jupyter more cleanly */\n",
       "    div.output_subarea {\n",
       "        /* Undo an unfortunate max-width parameter\n",
       "        that causes the output area to be too narrow\n",
       "        on smaller screens. */\n",
       "        max-width: none;\n",
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       "        /* Align the table with the content */\n",
       "        padding: 0;\n",
       "\n",
       "        /* Let it breathe */\n",
       "        margin-top: 20px;\n",
       "    }\n",
       "</style>\n",
       "\n",
       "<iframe class=\"mols2grid-iframe\" frameborder=\"0\" width=\"100%\"\n",
       "    \n",
       "    \n",
       "    allow=\"clipboard-write\"\n",
       "    \n",
       "    \n",
       "    sandbox=\"allow-scripts allow-same-origin allow-downloads allow-popups allow-modals\"\n",
       "    \n",
       "    srcdoc=\"\n",
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       "\n",
       "\n",
       "&lt;html lang=&quot;en&quot;&gt;\n",
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       "        &lt;title&gt;Document!&lt;/title&gt;\n",
       "\n",
       "\n",
       "\n",
       "        &lt;style&gt;\n",
       "            /**\n",
       " * General styling\n",
       " */\n",
       "body {\n",
       "    font-family: &#x27;DejaVu&#x27;, sans-serif;\n",
       "}\n",
       "h1,h2,h3,h4 {\n",
       "    margin: 0 0 10px 0;\n",
       "}\n",
       "h1 {\n",
       "    font-size: 26px;\n",
       "}\n",
       "h2 {\n",
       "    font-size: 20px;\n",
       "    font-weight: 400;\n",
       "}\n",
       "h3 {\n",
       "\tfont-size: 16px;\n",
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       "p {\n",
       "    margin: 0 0 10px 0;\n",
       "}\n",
       "\n",
       "\n",
       "/* Remove body margin inside iframe */\n",
       "body.m2g-inside-iframe {\n",
       "    margin: 0;\n",
       "}\n",
       "\n",
       "/* In-cell text */\n",
       "#mols2grid .data:not(.data-img) {\n",
       "    height: 16px;\n",
       "    line-height: 16px;\n",
       "}\n",
       "/* Text truncation */\n",
       "#mols2grid .data {\n",
       "    /* Break text into multiple lines (default for static)... */\n",
       "    word-wrap: normal;\n",
       "\n",
       "    /* ...or truncate it (default for interactive). */\n",
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       "\n",
       "\n",
       "/**\n",
       " * Popover\n",
       " * - - -\n",
       " * Note: this is a bootstrap variable which is not namespaced.\n",
       " * To avoid any contamination, we only style it when the\n",
       " * x-placement parameter is set.\n",
       " */\n",
       ".popover[x-placement] {\n",
       "    font-family: &#x27;DejaVu&#x27;, sans-serif;\n",
       "    background: white;\n",
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       "    border-radius: 5px;\n",
       "    box-shadow: 0 0 20px rgba(0,0,0,.15);\n",
       "    user-select: none;\n",
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       ".popover[x-placement] h3 {\n",
       "    margin: 0;\n",
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       ".popover[x-placement] .arrow {\n",
       "    width: 10px;\n",
       "    height: 10px;\n",
       "    background: #fff;\n",
       "    border: solid 1px rgba(0,0,0,.2);\n",
       "    box-sizing: border-box;\n",
       "    position: absolute;\n",
       "    transform-origin: 5px 5px;\n",
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       ".popover[x-placement=&#x27;left&#x27;] .arrow {\n",
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       ".popover[x-placement=&#x27;right&#x27;] .arrow {\n",
       "    transform: rotate(-135deg);\n",
       "    top: 50%;\n",
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       ".popover[x-placement=&#x27;top&#x27;] .arrow {\n",
       "    transform: rotate(135deg);\n",
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       ".popover[x-placement=&#x27;bottom&#x27;] .arrow {\n",
       "    transform: rotate(-45deg);\n",
       "    left: 50%;\n",
       "    top: -5px;\n",
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       "            body {\n",
       "    /* Colors */\n",
       "    --m2g-black: rgba(0,0,0,.75);\n",
       "    --m2g-black-soft: rgba(0,0,0,.35);\n",
       "    --m2g-black-10: rgba(0,0,0,.1);\n",
       "    --m2g-bg: #f6f6f6;\n",
       "    --m2g-border: solid 1px rgba(0,0,0,0.2);\n",
       "    --m2g-hl: #555; /* Highlight color */\n",
       "    --m2g-hl-shadow: inset 0 0 0 1px var(--m2g-hl); /* Inset 1px shadow to make border thicker */\n",
       "    --m2g-blue: #0f62fe;\n",
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       "    --m2g-icn-triangle: url(&#x27;data:image/svg+xml;utf8,&lt;svg width=&quot;20&quot; fill=&quot;rgba(0,0,0,.75)&quot; height=&quot;20&quot; viewBox=&quot;0 0 20 20&quot; xmlns=&quot;http://www.w3.org/2000/svg&quot;&gt;&lt;path d=&quot;M9.5713 13.285L6.2543 7.757C6.0543 7.424 6.2953 7 6.6823 7L13.3173 7C13.7053 7 13.9463 7.424 13.7453 7.757L10.4283 13.285C10.2343 13.609 9.7653 13.609 9.5713 13.285Z&quot;/&gt;&lt;/svg&gt;&#x27;);\n",
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       "    --m2g-icn-cb-white: url(&#x27;data:image/svg+xml;utf8,&lt;svg width=&quot;16&quot; height=&quot;16&quot; viewBox=&quot;0 0 16 16&quot; fill=&quot;none&quot; stroke=&quot;white&quot; stroke-width=&quot;2.5&quot; stroke-linecap=&quot;round&quot; xmlns=&quot;http://www.w3.org/2000/svg&quot;&gt;&lt;path d=&quot;M4 7.65686L7 10.6569L12.6569 5.00001&quot;/&gt;&lt;/svg&gt;&#x27;);\n",
       "    \n",
       "    /* Border radius */\n",
       "    --m2g-br: 3px;\n",
       "    --m2g-br-l: var(--m2g-br) 0 0 var(--m2g-br); /* Left-only */\n",
       "    --m2g-br-r: 0 var(--m2g-br) var(--m2g-br) 0; /* Right-only */\n",
       "\n",
       "    /* Text */\n",
       "    --m2g-fs: 14px; /* UI font-size */\n",
       "    --m2g-fs-cell: 12px; /* Cell font-size */\n",
       "\n",
       "    /* Transition speeds */\n",
       "    --m2g-trans: 150ms;\n",
       "\n",
       "    /* Layout */\n",
       "    --m2g-h: 40px; /* Form element height */\n",
       "}\n",
       "\n",
       "/* Styling */\n",
       "#mols2grid {\n",
       "    font-family: &#x27;DejaVu&#x27;, sans-serif;\n",
       "    font-size: var(--m2g-fs);\n",
       "}\n",
       "\n",
       "/* Fixes */\n",
       "#mols2grid *,\n",
       "#mols2grid *::before,\n",
       "#mols2grid *::after {\n",
       "    box-sizing: border-box;\n",
       "    outline: none;\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "/**\n",
       " * Functions section\n",
       " */\n",
       "\n",
       "#mols2grid .m2g-functions {\n",
       "    display: flex;\n",
       "}\n",
       "#mols2grid .m2g-functions .m2g-row {\n",
       "    flex: 0;\n",
       "    display: flex;\n",
       "}\n",
       "\n",
       "/* Individual elements don&#x27;t scale */\n",
       "#mols2grid .m2g-functions .m2g-row &gt; * {\n",
       "    flex: 0 0;\n",
       "    margin-right: 10px;\n",
       "}\n",
       "#mols2grid .m2g-functions .m2g-row:last-child &gt; *:last-child {\n",
       "    margin-right: 0;\n",
       "}\n",
       "\n",
       "/* Row 1: pagination + gap + sort */\n",
       "#mols2grid .m2g-functions .m2g-row:first-child {\n",
       "    flex: 1; /* Scale */\n",
       "}\n",
       "#mols2grid .m2g-functions .m2g-gap {\n",
       "    /* The gap in between will scale, so the pagination\n",
       "    stays on the left, while the rest moves to the right */\n",
       "    flex: 1;\n",
       "    margin-right: 0;\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "/*\n",
       " * Pagination\n",
       " */\n",
       "\n",
       "#mols2grid ul.m2g-pagination {\n",
       "    /* Unset defaults */\n",
       "    list-style-type: none;\n",
       "    margin-block-start: 0;\n",
       "    margin-block-end: 0;\n",
       "    margin-inline-start: 0;\n",
       "    margin-inline-end: 0;\n",
       "    padding-inline-start: 0;\n",
       "\n",
       "    /* Custom */\n",
       "    display: flex;\n",
       "}\n",
       "#mols2grid ul.m2g-pagination li {\n",
       "    background: var(--m2g-bg) ;\n",
       "    border: var(--m2g-border);\n",
       "    height: var(--m2g-h);\n",
       "    min-width: calc(var(--m2g-h) + 1px);\n",
       "    position: relative;\n",
       "    user-select: none;\n",
       "    \n",
       "    /* Compensate for double border */\n",
       "    margin-right: -1px;\n",
       "    \n",
       "    /* Center text */\n",
       "    display: flex;\n",
       "    align-items: center;\n",
       "    justify-content: center;\n",
       "}\n",
       "#mols2grid ul.m2g-pagination li:last-child {\n",
       "    min-width: var(--m2g-h);\n",
       "}\n",
       "#mols2grid ul.m2g-pagination li a {\n",
       "    text-decoration: none;\n",
       "    color: var(--m2g-black);\n",
       "    padding: 0 10px;\n",
       "    width: 100%;\n",
       "    height: var(--m2g-h);\n",
       "    line-height: var(--m2g-h);\n",
       "    text-align: center;\n",
       "    /* Compensate for border so there&#x27;s no gap between click areas  */\n",
       "    margin: 0 -1px;\n",
       "}\n",
       "\n",
       "/* Corner shape */\n",
       "#mols2grid ul.m2g-pagination li:first-child {\n",
       "    border-radius: var(--m2g-br-l);\n",
       "}\n",
       "#mols2grid ul.m2g-pagination li:last-child {\n",
       "    border-radius: var(--m2g-br-r);\n",
       "    margin-right: 0;\n",
       "}\n",
       "\n",
       "/* Focus state */\n",
       "#mols2grid ul.m2g-pagination li:focus-within {\n",
       "    border-color: var(--m2g-hl);\n",
       "    box-shadow: var(--m2g-hl-shadow);\n",
       "    z-index: 1;\n",
       "}\n",
       "\n",
       "/* Active state */\n",
       "#mols2grid ul.m2g-pagination li.active {\n",
       "    background: var(--m2g-hl);\n",
       "    z-index: 1;\n",
       "}\n",
       "#mols2grid ul.m2g-pagination li.active a {\n",
       "    cursor: default;\n",
       "    color: #fff;\n",
       "}\n",
       "\n",
       "/* Disabled sate */\n",
       "#mols2grid ul.m2g-pagination li.disabled a {\n",
       "    cursor: default;\n",
       "    color: rgba(0,0,0,.25);\n",
       "    pointer-events: none;\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "/*\n",
       " * Dropdowns\n",
       " */\n",
       "\n",
       "#mols2grid ::placeholder {\n",
       "    color: var(--m2g-black-soft);\n",
       "}\n",
       "#mols2grid .m2g-dropdown {\n",
       "    height: var(--m2g-h);\n",
       "    background: var(--m2g-bg);\n",
       "    border: var(--m2g-border);\n",
       "    border-radius: var(--m2g-br);\n",
       "    position: relative;\n",
       "}\n",
       "#mols2grid .m2g-dropdown select {\n",
       "    -webkit-appearance: none;\n",
       "    -moz-appearance: none;\n",
       "    -ms-appearance: none;\n",
       "    appearance: none;\n",
       "    background: transparent;\n",
       "    border: none;\n",
       "    height: 100%;\n",
       "    padding: 0 13px;\n",
       "    min-width: 0;\n",
       "    max-width: 250px;\n",
       "    color: var(--m2g-black);\n",
       "    cursor: pointer;\n",
       "}\n",
       "\n",
       "/* Icon */\n",
       "#mols2grid .m2g-dropdown .m2g-icon {\n",
       "    width: 30px;\n",
       "    height: var(--m2g-h);\n",
       "    display: flex;\n",
       "    align-items: center;\n",
       "    justify-content: center;\n",
       "    position: absolute;\n",
       "    top: 0;\n",
       "    right: 0;\n",
       "    pointer-events: none;\n",
       "}\n",
       "#mols2grid .m2g-dropdown .m2g-icon svg:not(.m2g-stroke) {\n",
       "    fill: var(--m2g-black);\n",
       "}\n",
       "#mols2grid .m2g-dropdown .m2g-icon svg.m2g-stroke {\n",
       "    stroke: var(--m2g-black);\n",
       "}\n",
       "\n",
       "/* Display */\n",
       "/* We hide the native select element because\n",
       " * it is limited in styling. Instead, we display\n",
       " * the selected value in a div. */\n",
       "#mols2grid .m2g-dropdown .m2g-display {\n",
       "    position: absolute;\n",
       "    left: 0;\n",
       "    right: 0;\n",
       "    top: 0;\n",
       "    bottom: 0;\n",
       "    pointer-events: none;\n",
       "    color: var(--m2g-black);\n",
       "    line-height: var(--m2g-h);\n",
       "    padding: 0 25px 0 13px;\n",
       "\n",
       "    /* Truncate dropdown text */\n",
       "    white-space: nowrap;\n",
       "\ttext-overflow: ellipsis;\n",
       "\toverflow: hidden;\n",
       "}\n",
       "\n",
       "/* Focus state */\n",
       "#mols2grid .m2g-dropdown:focus-within {\n",
       "    border-color: var(--m2g-hl);\n",
       "    box-shadow: var(--m2g-hl-shadow);\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "/**\n",
       " * Action dropdown\n",
       " */\n",
       "\n",
       "#mols2grid .m2g-dropdown.m2g-actions {\n",
       "    width: var(--m2g-h);\n",
       "    padding: 0;\n",
       "}\n",
       "#mols2grid .m2g-dropdown.m2g-actions select {\n",
       "    opacity: 0;\n",
       "    width: var(--m2g-h);\n",
       "}\n",
       "#mols2grid .m2g-dropdown.m2g-actions .m2g-icon {\n",
       "    width: var(--m2g-h);\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "/*\n",
       " * Sort dropdown\n",
       " */\n",
       "\n",
       "#mols2grid .m2g-dropdown.m2g-sort {\n",
       "    flex: 0 0 200px;\n",
       "    width: 200px; /* Needed in addition to flex-basis for small sizes! */\n",
       "    border-radius: var(--m2g-br);\n",
       "    background: var(--m2g-bg);\n",
       "    display: flex;\n",
       "}\n",
       "\n",
       "/* Dropdown */\n",
       "#mols2grid .m2g-dropdown.m2g-sort select {\n",
       "    flex: 1 1;\n",
       "    opacity: 0;\n",
       "    /* padding-right: 70px; Space for &quot;Sort:&quot; */\n",
       "    box-sizing: border-box;\n",
       "}\n",
       "\n",
       "/* Sort order */\n",
       "#mols2grid .m2g-dropdown.m2g-sort .m2g-order {\n",
       "    background: var(--m2g-bg) var(--m2g-icn-triangle) no-repeat center;\n",
       "    flex: 0 0 30px;\n",
       "    height: 100%;\n",
       "    border-left: var(--m2g-border);\n",
       "    cursor: pointer;\n",
       "}\n",
       "#mols2grid .m2g-dropdown.m2g-sort.m2d-arrow-desc .m2g-order {\n",
       "    transform: rotate(180deg);\n",
       "    border-left: none;\n",
       "    border-right: var(--m2g-border);\n",
       "}\n",
       "\n",
       "/* Display */\n",
       "#mols2grid .m2g-dropdown.m2g-sort .m2g-display {\n",
       "    right: 31px;\n",
       "    padding-right: 13px;\n",
       "}\n",
       "#mols2grid .m2g-dropdown.m2g-sort .m2g-display::before {\n",
       "    content: &#x27;Sort: &#x27;;\n",
       "}\n",
       "\n",
       "/* Focus state */\n",
       "#mols2grid .m2g-dropdown.m2g-sort:focus-within .m2g-display,\n",
       "#mols2grid .m2g-dropdown.m2g-sort:focus-within .m2g-order {\n",
       "    background-color: transparent;\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "/*\n",
       " * Search bar\n",
       " */\n",
       "\n",
       "#mols2grid .m2g-search-wrap {\n",
       "    height: var(--m2g-h);\n",
       "    display: flex;\n",
       "    align-items: center;\n",
       "    justify-content: flex-end;\n",
       "    background: var(--m2g-bg);\n",
       "    border: var(--m2g-border);\n",
       "    border-radius: var(--m2g-br);\n",
       "}\n",
       "#mols2grid .m2g-searchbar {\n",
       "    width: 170px;\n",
       "    height: var(--m2g-h);\n",
       "    padding: 0 13px;\n",
       "    border: none;\n",
       "    color: var(--m2g-black);\n",
       "    cursor: text;\n",
       "    background: transparent;\n",
       "}\n",
       "\n",
       "/* Focus state */\n",
       "#mols2grid .m2g-search-wrap:focus-within {\n",
       "    border-color: var(--m2g-hl);\n",
       "    box-shadow: var(--m2g-hl-shadow);\n",
       "}\n",
       "\n",
       "/* Option buttons */\n",
       "#mols2grid .m2g-search-options {\n",
       "    font-size: 12px;\n",
       "    display: flex;\n",
       "    height: calc(1.5em + .75rem);\n",
       "    line-height: calc(1.5em + .75rem);\n",
       "    margin-right: 5px;\n",
       "    border-radius: var(--m2g-br);\n",
       "    color: var(--m2g-black);\n",
       "}\n",
       "#mols2grid .m2g-search-options .m2g-option {\n",
       "    background: var(--m2g-black-10);\n",
       "    padding: 0 13px;\n",
       "    cursor: default;\n",
       "    user-select: none;\n",
       "}\n",
       "#mols2grid .m2g-search-options .m2g-option:not(.sel) {\n",
       "    cursor: pointer;\n",
       "}\n",
       "#mols2grid .m2g-search-options .m2g-option:first-child {\n",
       "    border-radius: 2px 0 0 2px;\n",
       "}\n",
       "#mols2grid .m2g-search-options .m2g-option:last-child {\n",
       "    border-radius: 0 2px 2px 0;\n",
       "}\n",
       "#mols2grid .m2g-search-options .m2g-option.sel {\n",
       "    background: var(--m2g-hl);\n",
       "    color: #fff;\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "/**\n",
       " * Grid\n",
       " */\n",
       "\n",
       "/* Container */\n",
       "#mols2grid .m2g-list {\n",
       "    display: flex;\n",
       "    flex-wrap: wrap;\n",
       "    align-items: flex-start;\n",
       "    justify-content: flex-start;\n",
       "    padding: 1px; /* Compensate for negative padding on cell */\n",
       "    user-select: none;\n",
       "    margin: 0px;\n",
       "    margin-top: 20px;\n",
       "    font-family: &#x27;DejaVu&#x27;, sans-serif;\n",
       "    \n",
       "}\n",
       "\n",
       "/* Cell */\n",
       "#mols2grid .m2g-cell {\n",
       "    border: 1px solid #cccccc;\n",
       "    text-align: center;\n",
       "    vertical-align: top;\n",
       "    font-family: var(--font-family);\n",
       "    padding: 10px;\n",
       "    padding-top: max(10px, 20px);\n",
       "    margin: -1px -1px 0 0;\n",
       "    flex: 1 0 320px;\n",
       "    position: relative;\n",
       "    font-size: var(--m2g-fs-cell);\n",
       "    cursor: pointer;\n",
       "    color: var(--m2g-black);\n",
       "    overflow: hidden;\n",
       "    box-sizing: border-box;\n",
       "    background-color: white;\n",
       "}\n",
       "#mols2grid .m2g-cell:focus {\n",
       "    z-index: 1;\n",
       "    border-color: var(--m2g-hl);\n",
       "    box-shadow: var(--m2g-hl-shadow);\n",
       "}\n",
       "\n",
       "/* Phantom cells to maintain grid structure with less results */\n",
       "#mols2grid .m2g-cell.m2g-phantom {\n",
       "    border: none;\n",
       "    pointer-events: none;\n",
       "    height: 0;\n",
       "    padding: 0;\n",
       "}\n",
       "\n",
       "/* Checkbox &amp; ID */\n",
       "#mols2grid .m2g-cb-wrap {\n",
       "    position: absolute;\n",
       "    top: 3px;\n",
       "    left: 3px;\n",
       "    display: flex;\n",
       "    border-radius: 2px;\n",
       "    font-size: 0;\n",
       "    line-height: 0;\n",
       "    padding: 3px;\n",
       "    padding-right: 0;\n",
       "}\n",
       "#mols2grid .m2g-cb-wrap input[type=checkbox] {\n",
       "    display: none;\n",
       "}\n",
       "#mols2grid .m2g-cb-wrap input[type=checkbox] + .m2g-cb {\n",
       "\twidth: 16px;\n",
       "\theight: 16px;\n",
       "\tbox-sizing: border-box;\n",
       "\tbackground: #fff;\n",
       "\tborder: var(--m2g-border);\n",
       "\tborder-radius: 2px;\n",
       "    margin-right: 5px;\n",
       "}\n",
       "#mols2grid .m2g-cb-wrap input[type=checkbox]:checked + .m2g-cb {\n",
       "    border: none;\n",
       "    background-color: var(--m2g-blue);\n",
       "    background-image: var(--m2g-icn-cb-white);\n",
       "}\n",
       "#mols2grid .m2g-tooltip {\n",
       "    /* This is a div spanning full cell size where the\n",
       "    tooltip is rendered around, because you can&#x27;t attach\n",
       "    it to the parent due to list.js limitation. */\n",
       "    width: 100%;\n",
       "    height: 100%;\n",
       "    position: absolute;\n",
       "    left: 0;\n",
       "    top: 0;\n",
       "    z-index: -1;\n",
       "    pointer-events: none;\n",
       "    opacity: 0;\n",
       "}\n",
       "#mols2grid .m2g-cell:has(:checked) {\n",
       "    background: #ffd !important; /* Overrides user-set background color */\n",
       "}\n",
       "#mols2grid .data-mols2grid-id-display {\n",
       "    font-size: var(--m2g-fs-cell);\n",
       "    line-height: 16px;\n",
       "}\n",
       "#mols2grid .m2g-cb-wrap input[type=checkbox] + .data-mols2grid-id-display {\n",
       "    padding: 0 5px 0 5px;\n",
       "}\n",
       "#mols2grid .m2g-cb-wrap .data-name-display {\n",
       "    font-size: var(--m2g-fs);\n",
       "    line-height: 16px;\n",
       "}\n",
       "\n",
       "/* Info + callback button wrap (28px high) */\n",
       "#mols2grid .m2g-cell-actions {\n",
       "    position: absolute;\n",
       "    top: 0;\n",
       "    right: 0;\n",
       "    display: flex;\n",
       "    flex-direction: row;\n",
       "    font-size: 0;\n",
       "    line-height: 0;\n",
       "    \n",
       "    /* background: yellow; */\n",
       "}\n",
       "\n",
       "/* Info button */\n",
       "#mols2grid .m2g-info {\n",
       "    width: 28px;\n",
       "    height: 28px;\n",
       "    border-radius: 2px;\n",
       "    line-height: 28px;\n",
       "    font-size: min(14px, 12px);\n",
       "    font-family: Georgia, serif;\n",
       "    font-style: italic;\n",
       "    padding: 0;\n",
       "    text-align: center;\n",
       "}\n",
       "#mols2grid .m2g-keep-tooltip .m2g-info {\n",
       "    color: #fff;\n",
       "}\n",
       "#mols2grid .m2g-keep-tooltip .m2g-info::before {\n",
       "    content: &#x27;i&#x27;;\n",
       "    width: 18px;\n",
       "    height: 18px;\n",
       "    line-height: 18px;\n",
       "    background: var(--m2g-hl);\n",
       "    position: absolute;\n",
       "    left: 5px;\n",
       "    top: 5px;\n",
       "    border-radius: 9px;\n",
       "}\n",
       "\n",
       "/* Callback button */\n",
       "#mols2grid .m2g-callback {\n",
       "    width: 28px;\n",
       "    height: 28px;\n",
       "    cursor: pointer;\n",
       "}\n",
       "#mols2grid .m2g-callback::after {\n",
       "    content: &#x27;&#x27;;\n",
       "    display: block;\n",
       "    width: 16px;\n",
       "    height: 16px;\n",
       "    margin: 6px;\n",
       "    border: var(--m2g-border);\n",
       "    border-radius: 2px;\n",
       "    background: var(--m2g-bg) var(--m2g-icn-triangle) no-repeat center;\n",
       "    transform: rotate(-90deg);\n",
       "}\n",
       "\n",
       "/* Image */\n",
       "#mols2grid .m2g-cell .data-img {\n",
       "    padding: 0;\n",
       "}\n",
       "#mols2grid .m2g-cell img,\n",
       "#mols2grid .m2g-cell svg {\n",
       "    max-width: 100%;\n",
       "    height: auto;\n",
       "    padding: 0;\n",
       "}\n",
       "#mols2grid .m2g-cell svg &gt; rect:first-child {\n",
       "    /* Remove the SVG background */\n",
       "    fill: transparent !important;\n",
       "}\n",
       "\n",
       "/* Text below image */\n",
       ".m2g-copy-blink {\n",
       "    animation: m2g-blink var(--m2g-trans) 3;\n",
       "}\n",
       "@keyframes m2g-blink {\n",
       "    0% {\n",
       "        opacity: 1;\n",
       "    }\n",
       "    49% {\n",
       "        opacity: 1;\n",
       "    }\n",
       "    50% {\n",
       "        opacity: 0;\n",
       "    }\n",
       "    100% {\n",
       "        opacity: 0;\n",
       "    }\n",
       "}\n",
       "\n",
       "/* Copyable text */\n",
       ".copy-me {\n",
       "    position: relative;\n",
       "    cursor: pointer;\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "/**\n",
       " * Modal popup\n",
       " * - - -\n",
       " * Triggered by make_popup_callback()\n",
       " * See https://mols2grid.readthedocs.io/en/latest/notebooks/callbacks.html#Display-a-popup-containing-descriptors\n",
       " */\n",
       "\n",
       "/* Container */\n",
       "#m2g-modal-container {\n",
       "    display: flex;\n",
       "    align-items: center;\n",
       "    justify-content: center;\n",
       "    background: var(--m2g-black-10);\n",
       "    position: fixed;\n",
       "    top: 0;\n",
       "    left: 0;\n",
       "    z-index: 1;\n",
       "    width: 100%;\n",
       "    height: 100%;\n",
       "    \n",
       "    /* Transition */\n",
       "    opacity: 0;\n",
       "    transition: opacity var(--m2g-trans) linear;\n",
       "}\n",
       "\n",
       "/* Modal */\n",
       "#m2g-modal {\n",
       "    background: #fff;\n",
       "    border-radius: var(--m2g-br);\n",
       "    box-shadow: 0 0 30px var(--m2g-black-10);\n",
       "    padding: 20px;\n",
       "    position: relative;\n",
       "    max-width: calc(100% - 80px);\n",
       "    max-height: calc(100% - 80px);\n",
       "    display: flex;\n",
       "    flex-direction: column;\n",
       "    min-width: 26px;\n",
       "\n",
       "    /* Transition */\n",
       "    opacity: 0;\n",
       "    transform: translate(0, 5px);\n",
       "    transition: transform var(--m2g-trans) ease-in-out, opacity var(--m2g-trans) linear;\n",
       "}\n",
       "#m2g-modal .m2g-modal-header {\n",
       "    flex: 0 0 26px;\n",
       "    margin-bottom: 10px;\n",
       "}\n",
       "#m2g-modal .m2g-modal-header h2 {\n",
       "    margin-bottom: 0;\n",
       "}\n",
       "#m2g-modal .m2g-modal-header h2 + p {\n",
       "    font-size: 15px;\n",
       "}\n",
       "#m2g-modal .m2g-modal-body {\n",
       "    flex: 1;\n",
       "    position: relative;\n",
       "}\n",
       "\n",
       "/* Transition */\n",
       "#m2g-modal-container.show {\n",
       "    opacity: 1;\n",
       "}\n",
       "#m2g-modal-container.show #m2g-modal {\n",
       "    opacity: 1;\n",
       "    transform: translate(0, 0);\n",
       "}\n",
       "\n",
       "/* Header + close btn */\n",
       "#m2g-modal h2 {\n",
       "    line-height: 26px;\n",
       "    padding-right: 40px;\n",
       "    text-transform: capitalize;\n",
       "}\n",
       "#m2g-modal h3 {\n",
       "    \n",
       "}\n",
       "#m2g-modal button.close {\n",
       "    background: transparent;\n",
       "    padding: 0;\n",
       "    color: var(--m2g-black);\n",
       "    font-size: 1.5rem;\n",
       "    width: 40px;\n",
       "    height: 40px;\n",
       "    position: absolute;\n",
       "    top: 13px;\n",
       "    right: 13px;\n",
       "    border: none;\n",
       "}\n",
       "\n",
       "/* Image */\n",
       "#m2g-modal .svg-wrap svg {\n",
       "    max-width: 100%;\n",
       "    margin-bottom: 20px;\n",
       "}\n",
       "\n",
       "/* Separator */\n",
       "hr {\n",
       "    width: 100%;\n",
       "    height: 1px;\n",
       "    background: #ddd;\n",
       "    margin: 15px 0;\n",
       "    border: none;\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "/**\n",
       " * Hover states\n",
       " */\n",
       "@media (hover:hover) {\n",
       "    /* Pagination */\n",
       "    #mols2grid ul.m2g-pagination li:not(.active):not(.disabled):hover {\n",
       "        background: #f0f0f0;\n",
       "        z-index: 1;\n",
       "    }\n",
       "    #mols2grid ul.m2g-pagination li.active + li:hover {\n",
       "        /* Keeping the hover border consiistent */\n",
       "        margin-left: 1px;\n",
       "        border-left: none;\n",
       "        min-width: 40px;\n",
       "    }\n",
       "\n",
       "    /* Dropdowns &amp; search */\n",
       "    #mols2grid .m2g-dropdown:not(:focus-within):hover,\n",
       "    #mols2grid .m2g-search-wrap:not(:focus-within):hover,\n",
       "    #mols2grid .m2g-sort:not(:focus-within) .m2g-order:hover {\n",
       "        background-color: #f0f0f0;\n",
       "    }\n",
       "    #mols2grid .m2g-search-wrap:not(:focus-within):hover {\n",
       "        background: #fff;\n",
       "        border-color: rgba(0,0,0,.3);\n",
       "    }\n",
       "    /* Hocus pocus to have separate hover states for dropdown and arrow */\n",
       "    #mols2grid .m2g-dropdown.m2g-sort:not(:focus-within):hover .m2g-order:not(:hover) + .m2g-display {\n",
       "        background-color: transparent;\n",
       "    }\n",
       "\n",
       "    /* Search options */\n",
       "    #mols2grid .m2g-search-options .m2g-option:not(.sel):hover {\n",
       "        background: rgba(0,0,0,.15);\n",
       "    }\n",
       "\n",
       "    /* Grid */\n",
       "    /* Note: this is in an ::after pseudo element, so the transparent\n",
       "    hover color plays nice with the cell background color. */\n",
       "    #mols2grid .m2g-cell:hover::after {\n",
       "        content: &#x27;&#x27;;\n",
       "        width: 100%;\n",
       "        height: 100%;\n",
       "        position: absolute;\n",
       "        top: 0;\n",
       "        left: 0;\n",
       "        background-color: rgba(0,0,0,0.05);\n",
       "        pointer-events: none;\n",
       "    }\n",
       "\n",
       "    /* info button */\n",
       "    #mols2grid .m2g-info:hover::before {\n",
       "        content: &#x27;i&#x27;;\n",
       "        color: #fff;\n",
       "        width: 18px;\n",
       "        height: 18px;\n",
       "        line-height: 18px;\n",
       "        background: var(--m2g-hl);\n",
       "        position: absolute;\n",
       "        left: 5px;\n",
       "        top: 5px;\n",
       "        border-radius: 9px;\n",
       "    }\n",
       "    \n",
       "    /* Callback button */\n",
       "    #mols2grid .m2g-callback:hover::after {\n",
       "        background-color: var(--m2g-black);\n",
       "        background-image: var(--m2g-icn-triangle-white);\n",
       "        border-color: transparent;\n",
       "    }\n",
       "\n",
       "    /* Copyable text */\n",
       "    .copy-me:hover {\n",
       "        text-decoration: underline;\n",
       "        text-decoration-color: var(--m2g-blue);\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "/**\n",
       " * Responsive behavior.\n",
       " * - - -\n",
       " * Note: container queries won&#x27;t work in older browsers,\n",
       " * but this is purely aesthetical behavior so that&#x27;s ok.\n",
       " * https://caniuse.com/css-container-queries\n",
       " */\n",
       "\n",
       "/* This sets the msg-list div as reference container */\n",
       "#mols2grid {\n",
       "    container-type: inline-size;\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "/**\n",
       " * Functions section\n",
       " */\n",
       "\n",
       "/* When there&#x27;s not enough space to put everything in one row, we break it into two.\n",
       " * - - -\n",
       " * 870px = pagination 280 + sort 200 + search 300 + menu 40 + (3*10 gap) = 850 + 20 buffer.\n",
       " * Buffer required because the button width inside the search depends on the font.\n",
       " */\n",
       "@container (max-width: 870px) {\n",
       "    #mols2grid .m2g-functions {\n",
       "        flex-direction: column-reverse;\n",
       "        gap: 10px;\n",
       "    }\n",
       "    #mols2grid .m2g-functions .m2g-row:last-child {\n",
       "        justify-content: flex-end;\n",
       "    }\n",
       "    #mols2grid .m2g-functions .m2g-row:first-child *:last-child {\n",
       "        margin-right: 0;\n",
       "    }\n",
       "}\n",
       "\n",
       "/* When there&#x27;s not enough room for pagination + sort on one row,\n",
       " * we reduce the sort drodpwon width.\n",
       " */\n",
       "@container (max-width: 500px) {\n",
       "    #mols2grid .m2g-functions .m2g-sort {\n",
       "        width: 80px;\n",
       "        flex-basis: 80px;\n",
       "    }\n",
       "    #mols2grid .m2g-functions .m2g-sort .m2g-display {\n",
       "        font-size: 0;\n",
       "        line-height: 0;\n",
       "        padding-right: 0;\n",
       "    }\n",
       "    #mols2grid .m2g-functions .m2g-sort .m2g-display::before {\n",
       "        content: &#x27;Sort&#x27;;\n",
       "        font-size: var(--m2g-fs);\n",
       "        line-height: var(--m2g-h);\n",
       "    }\n",
       "}\n",
       "\n",
       "/* When there&#x27;s not enough room for pagination + reduced sort on one row,\n",
       " * we reduce the pagination width.\n",
       " */\n",
       "@container (max-width: 500px) {\n",
       "    /* We&#x27;re overriding min-width from different\n",
       "    locations, including responsive rules */\n",
       "    #mols2grid ul.m2g-pagination li,\n",
       "    #mols2grid ul.m2g-pagination li:last-child,\n",
       "    #mols2grid ul.m2g-pagination li.active + li:hover {\n",
       "        min-width: 0;\n",
       "    }\n",
       "}\n",
       "\n",
       "/* When there&#x27;s not enough room for searchbar + menu\n",
       " * we scale down the searchbar to fit the container.\n",
       " */\n",
       "@container (max-width: 370px) {\n",
       "    #mols2grid .m2g-functions .m2g-row .m2g-search-wrap {\n",
       "        flex: 1;\n",
       "    }\n",
       "    #mols2grid .m2g-searchbar {\n",
       "        width: calc(100% - 50px);\n",
       "    }\n",
       "    #mols2grid .m2g-search-options {\n",
       "        width: 50px;\n",
       "    }\n",
       "\n",
       "    /* Collapse options in T/M buttons */\n",
       "    #mols2grid .m2g-search-options .m2g-option {\n",
       "        width: 25px;\n",
       "        text-align: center;\n",
       "        padding: 0;\n",
       "        overflow: hidden;\n",
       "    }\n",
       "    #mols2grid .m2g-search-options .m2g-option:first-child::before {\n",
       "        content: &#x27;T\\A&#x27;\n",
       "    }\n",
       "    #mols2grid .m2g-search-options .m2g-option:last-child::before {\n",
       "        content: &#x27;S\\A&#x27;\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "/**\n",
       " * Grid\n",
       " */\n",
       "\n",
       "/* When there&#x27;s room for 5 columns, fall back to 4 */\n",
       "@container (min-width: 1279px) and (max-width: 1919px) {\n",
       "    #mols2grid .m2g-cell {\n",
       "        flex-basis: calc(100% / 4);\n",
       "    }\n",
       "}\n",
       "\n",
       "/* When there&#x27;s room for 7-11 columns, fall back to 6 */\n",
       "@container (min-width: 1919px) and (max-width: 3839px) {\n",
       "    #mols2grid .m2g-cell {\n",
       "        flex-basis: calc(100% / 6);\n",
       "    }\n",
       "}\n",
       "\n",
       "/* When there&#x27;s room for 13+ columns, fall back to 12 */\n",
       "@container (min-width: 3839px) {\n",
       "    #mols2grid .m2g-cell {\n",
       "        flex-basis: calc(100% / 12);\n",
       "    }\n",
       "}\n",
       "\n",
       "            /* Custom CSS */\n",
       "            \n",
       "        &lt;/style&gt;\n",
       "        &lt;script src=&quot;https://cdnjs.cloudflare.com/ajax/libs/list.js/2.3.1/list.min.js&quot;&gt;&lt;/script&gt;\n",
       "&lt;script src=&quot;https://code.jquery.com/jquery-3.6.0.min.js&quot; integrity=&quot;sha256-/xUj+3OJU5yExlq6GSYGSHk7tPXikynS7ogEvDej/m4=&quot; crossorigin=&quot;anonymous&quot;&gt;&lt;/script&gt;\n",
       "&lt;script src=&quot;https://unpkg.com/@rdkit/rdkit@2022.3.1/Code/MinimalLib/dist/RDKit_minimal.js&quot;&gt;&lt;/script&gt;\n",
       "        &lt;script&gt;\n",
       "    // Set iframe height to fit content.\n",
       "    function fitIframe(iframe) {\n",
       "        // Ignore when there&#x27;s no iframe\n",
       "        if (!iframe) return\n",
       "\n",
       "        // Only fit height when no specific height was given.\n",
       "        if (iframe.getAttribute(&#x27;height&#x27;)) return;\n",
       "\n",
       "        // Initial fit + refit whenever the window width changes.\n",
       "        _fit()\n",
       "        $(window).on(&#x27;resize&#x27;, function() {\n",
       "            if (window.innerWidth != window.prevInnerWidth) {\n",
       "                window.prevInnerWidth = window.innerWidth\n",
       "                _fit();\n",
       "            }\n",
       "        })\n",
       "\n",
       "        // Fit iframe height to content height.\n",
       "        function _fit() {\n",
       "            var height = iframe.contentDocument.body.scrollHeight + 18 + &#x27;px&#x27;;\n",
       "            iframe.style.height = height;\n",
       "        }\n",
       "    }\n",
       "&lt;/script&gt;\n",
       "\n",
       "&lt;!-- prettier-ignore --&gt;\n",
       "&lt;script src=&quot;https://cdn.jsdelivr.net/npm/bootstrap@4.6.0/dist/js/bootstrap.bundle.min.js&quot; integrity=&quot;sha384-Piv4xVNRyMGpqkS2by6br4gNJ7DXjqk09RmUpJ8jgGtD7zP9yug3goQfGII0yAns&quot; crossorigin=&quot;anonymous&quot;&gt;&lt;/script&gt;\n",
       "        \n",
       "        &lt;!-- Custom header --&gt;\n",
       "        \n",
       "\n",
       "\n",
       "\n",
       "\n",
       "    &lt;/head&gt;\n",
       "    &lt;body class=&quot;m2g-inside-iframe&quot;&gt;\n",
       "\n",
       "\n",
       "\n",
       "        &lt;div id=&quot;mols2grid&quot; class=&quot;grid-default&quot;&gt;\n",
       "            &lt;!-- Pagination &amp; search --&gt;\n",
       "            &lt;div class=&quot;m2g-functions&quot;&gt;\n",
       "                \n",
       "                &lt;div class=&quot;m2g-row&quot;&gt;\n",
       "                    &lt;!-- Pagination --&gt;\n",
       "                    &lt;ul class=&quot;m2g-pagination&quot; class=&quot;d-flex&quot;&gt;&lt;/ul&gt;\n",
       "                    &lt;div class=&quot;m2g-gap&quot;&gt;&lt;/div&gt;\n",
       "\n",
       "                    &lt;!-- Sort dropdown --&gt;\n",
       "                    &lt;div class=&quot;m2g-dropdown m2g-sort&quot;&gt;\n",
       "                        &lt;select&gt;\n",
       "                            \n",
       "                            \n",
       "                                \n",
       "                                \n",
       "                                \n",
       "                            &lt;option value=&quot;mols2grid-id&quot; selected&gt;Index&lt;/option&gt;\n",
       "                                \n",
       "                            \n",
       "                            \n",
       "                            &lt;option value=&quot;checkbox&quot;&gt;Selected&lt;/option&gt;\n",
       "                            \n",
       "                        &lt;/select&gt;\n",
       "                        &lt;div class=&quot;m2g-order&quot;&gt;&lt;/div&gt;\n",
       "                        &lt;div class=&quot;m2g-display&quot;&gt;\n",
       "                            Index\n",
       "                        &lt;/div&gt;\n",
       "                    &lt;/div&gt;\n",
       "                &lt;/div&gt;\n",
       "                &lt;div class=&quot;m2g-row&quot;&gt;\n",
       "                    &lt;!-- Search bar --&gt;\n",
       "                    &lt;div class=&quot;m2g-search-wrap&quot;&gt;\n",
       "                        &lt;input\n",
       "                            type=&quot;text&quot;\n",
       "                            class=&quot;m2g-searchbar form-control&quot;\n",
       "                            placeholder=&quot;Search&quot;\n",
       "                            aria-label=&quot;Search&quot;\n",
       "                            aria-describedby=&quot;basic-addon1&quot;\n",
       "                        /&gt;\n",
       "                        &lt;div class=&quot;m2g-search-options&quot;&gt;\n",
       "                            &lt;div class=&quot;m2g-option m2g-search-text sel&quot;&gt;Text&lt;/div&gt;\n",
       "                            &lt;div class=&quot;m2g-option m2g-search-smarts&quot;&gt;SMARTS&lt;/div&gt;\n",
       "                        &lt;/div&gt;\n",
       "                    &lt;/div&gt;\n",
       "\n",
       "                    &lt;!-- Action dropdown --&gt;\n",
       "                    &lt;div class=&quot;m2g-dropdown m2g-actions&quot;&gt;\n",
       "                        &lt;select&gt;\n",
       "                            &lt;option hidden&gt;-&lt;/option&gt;\n",
       "                            &lt;option value=&quot;select-all&quot;&gt;Select all&lt;/option&gt;\n",
       "                            &lt;option value=&quot;select-matching&quot;&gt;Select matching&lt;/option&gt;\n",
       "                            &lt;option value=&quot;unselect-all&quot;&gt;Unselect all&lt;/option&gt;\n",
       "                            &lt;option value=&quot;invert&quot;&gt;Invert&lt;/option&gt;\n",
       "                            &lt;option value=&quot;copy&quot;&gt;Copy to clipboard&lt;/option&gt;\n",
       "                            &lt;option value=&quot;save-smiles&quot;&gt;Save SMILES&lt;/option&gt;\n",
       "                            &lt;option value=&quot;save-csv&quot;&gt;Save CSV&lt;/option&gt;\n",
       "                        &lt;/select&gt;\n",
       "                        &lt;div class=&quot;m2g-icon&quot;&gt;\n",
       "                            &lt;svg width=&quot;20&quot; height=&quot;20&quot; viewBox=&quot;0 0 20 20&quot; xmlns=&quot;http://www.w3.org/2000/svg&quot;&gt;\n",
       "                                &lt;path d=&quot;M11.5 4C11.5 4.82843 10.8284 5.5 10 5.5C9.17157 5.5 8.5 4.82843 8.5 4C8.5 3.17157 9.17157 2.5 10 2.5C10.8284 2.5 11.5 3.17157 11.5 4ZM11.5 10C11.5 10.8284 10.8284 11.5 10 11.5C9.17157 11.5 8.5 10.8284 8.5 10C8.5 9.17157 9.17157 8.5 10 8.5C10.8284 8.5 11.5 9.17157 11.5 10ZM10 17.5C10.8284 17.5 11.5 16.8284 11.5 16C11.5 15.1716 10.8284 14.5 10 14.5C9.17157 14.5 8.5 15.1716 8.5 16C8.5 16.8284 9.17157 17.5 10 17.5Z&quot;/&gt;\n",
       "                            &lt;/svg&gt;\n",
       "                        &lt;/div&gt;\n",
       "                    &lt;/div&gt;\n",
       "                &lt;/div&gt;\n",
       "            &lt;/div&gt;\n",
       "\n",
       "            &lt;!-- Grid --&gt;\n",
       "            \n",
       "            &lt;div class=&quot;m2g-list&quot;&gt;&lt;div class=&quot;m2g-cell&quot; data-mols2grid-id=&quot;0&quot; tabindex=&quot;0&quot;&gt;&lt;div class=&quot;m2g-cb-wrap&quot;&gt;&lt;input type=&quot;checkbox&quot; tabindex=&quot;-1&quot; class=&quot;position-relative float-left cached_checkbox&quot;&gt;&lt;div class=&quot;m2g-cb&quot;&gt;&lt;/div&gt;&lt;div class=&quot;data-mols2grid-id-display&quot;&gt;&lt;/div&gt;&lt;/div&gt;&lt;div class=&quot;m2g-cell-actions&quot;&gt;&lt;/div&gt;&lt;div class=&quot;data data-img copy-me&quot;&gt;&lt;/div&gt;&lt;div class=&quot;data data-SMILES copy-me&quot; style=&quot;display: none;&quot;&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;\n",
       "        &lt;/div&gt;\n",
       "        &lt;script&gt;\n",
       "            // list.js\n",
       "var listObj = new List(&#x27;mols2grid&#x27;, {\n",
       "    listClass: &#x27;m2g-list&#x27;,\n",
       "    valueNames: [{data: [&#x27;mols2grid-id&#x27;]}, &#x27;data-mols2grid-id&#x27;, &#x27;data-SMILES&#x27;, &#x27;data-img&#x27;, &#x27;data-mols2grid-id-display&#x27;],\n",
       "    item: &#x27;&lt;div class=&quot;m2g-cell&quot; data-mols2grid-id=&quot;0&quot; tabindex=&quot;0&quot;&gt;&lt;div class=&quot;m2g-cb-wrap&quot;&gt;&lt;input type=&quot;checkbox&quot; tabindex=&quot;-1&quot; class=&quot;position-relative float-left cached_checkbox&quot;&gt;&lt;div class=&quot;m2g-cb&quot;&gt;&lt;/div&gt;&lt;div class=&quot;data-mols2grid-id-display&quot;&gt;&lt;/div&gt;&lt;/div&gt;&lt;div class=&quot;m2g-cell-actions&quot;&gt;&lt;/div&gt;&lt;div class=&quot;data data-img copy-me&quot;&gt;&lt;/div&gt;&lt;div class=&quot;data data-SMILES copy-me&quot; style=&quot;display: none;&quot;&gt;&lt;/div&gt;&lt;/div&gt;&#x27;,\n",
       "    page: 24,\n",
       "    pagination: {\n",
       "        paginationClass: &quot;m2g-pagination&quot;,\n",
       "        item: &#x27;&lt;li class=&quot;page-item&quot;&gt;&lt;a class=&quot;page page-link&quot; href=&quot;#&quot; onclick=&quot;event.preventDefault()&quot;&gt;&lt;/a&gt;&lt;/li&gt;&#x27;,\n",
       "        innerWindow: 1,\n",
       "        outerWindow: 1,\n",
       "    },\n",
       "});\n",
       "listObj.remove(&quot;mols2grid-id&quot;, &quot;0&quot;);\n",
       "listObj.add([{&quot;mols2grid-id&quot;: 0, &quot;data-SMILES&quot;: &quot;CCOc1ccc(N2C(=O)NC(=O)/C(=C\\\\c3ccc(OCC(=O)O)cc3)C2=O)cc1&quot;, &quot;data-img&quot;: null, &quot;data-mols2grid-id-display&quot;: 0}, {&quot;mols2grid-id&quot;: 1, &quot;data-SMILES&quot;: &quot;COc1ccc(N2C(=O)NC(=O)/C(=C\\\\c3ccc(O[C@H](C)C(=O)O)cc3)C2=O)cc1&quot;, &quot;data-img&quot;: null, &quot;data-mols2grid-id-display&quot;: 1}, {&quot;mols2grid-id&quot;: 2, &quot;data-SMILES&quot;: &quot;COc1ccccc1N1C(=O)NC(=O)/C(=C/c2ccc(N3CCOCC3)cc2)C1=O&quot;, &quot;data-img&quot;: null, &quot;data-mols2grid-id-display&quot;: 2}, {&quot;mols2grid-id&quot;: 3, &quot;data-SMILES&quot;: &quot;COc1cccc(N2C(=O)NC(=O)/C(=C/c3ccc(OC)c(OC)c3OC)C2=O)c1&quot;, &quot;data-img&quot;: null, &quot;data-mols2grid-id-display&quot;: 3}, {&quot;mols2grid-id&quot;: 4, &quot;data-SMILES&quot;: &quot;COc1cc(/C=C2\\\\C(=O)NC(=O)N(c3cccc(C)c3)C2=O)ccc1OCC(=O)O&quot;, &quot;data-img&quot;: null, 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&quot;data-mols2grid-id-display&quot;: 14}, {&quot;mols2grid-id&quot;: 15, &quot;data-SMILES&quot;: &quot;COc1cccc(N2C(=O)NC(=O)/C(=C\\\\c3ccc(OC)c(OC)c3OC)C2=O)c1&quot;, &quot;data-img&quot;: null, &quot;data-mols2grid-id-display&quot;: 15}, {&quot;mols2grid-id&quot;: 16, &quot;data-SMILES&quot;: &quot;COc1ccc(N2C(=O)NC(=O)/C(=C/c3c(OC)cc(OC)cc3OC)C2=O)cc1&quot;, &quot;data-img&quot;: null, &quot;data-mols2grid-id-display&quot;: 16}, {&quot;mols2grid-id&quot;: 17, &quot;data-SMILES&quot;: &quot;COc1cccc(N2C(=O)NC(=O)/C(=C\\\\c3cc(OC)c(OC)c(OC)c3)C2=O)c1&quot;, &quot;data-img&quot;: null, &quot;data-mols2grid-id-display&quot;: 17}, {&quot;mols2grid-id&quot;: 18, &quot;data-SMILES&quot;: &quot;COc1ccc(N2C(=O)NC(=O)/C(=C\\\\c3ccc(O[C@@H](C)C(=O)O)cc3)C2=O)cc1&quot;, &quot;data-img&quot;: null, &quot;data-mols2grid-id-display&quot;: 18}, {&quot;mols2grid-id&quot;: 19, &quot;data-SMILES&quot;: &quot;COc1cccc(N2C(=O)NC(=O)/C(=C/c3cc(OC)c(OC)cc3OC)C2=O)c1&quot;, &quot;data-img&quot;: null, &quot;data-mols2grid-id-display&quot;: 19}, {&quot;mols2grid-id&quot;: 20, &quot;data-SMILES&quot;: &quot;COc1cccc(N2C(=O)NC(=O)/C(=C/c3c(OC)cc(OC)cc3OC)C2=O)c1&quot;, &quot;data-img&quot;: null, &quot;data-mols2grid-id-display&quot;: 20}, {&quot;mols2grid-id&quot;: 21, &quot;data-SMILES&quot;: &quot;CCOc1cccc(N2C(=O)NC(=O)/C(=C/c3cc4c(cc3OC)OCO4)C2=O)c1&quot;, &quot;data-img&quot;: null, &quot;data-mols2grid-id-display&quot;: 21}, {&quot;mols2grid-id&quot;: 22, &quot;data-SMILES&quot;: &quot;CCOc1ccccc1N1C(=O)NC(=O)/C(=C\\\\c2cc(OC)c(O)c(OC)c2)C1=O&quot;, &quot;data-img&quot;: null, &quot;data-mols2grid-id-display&quot;: 22}, {&quot;mols2grid-id&quot;: 23, &quot;data-SMILES&quot;: &quot;Cc1ccc(Cl)cc1N1C(=O)NC(=O)/C(=C\\\\c2ccc(OCC(N)=O)cc2)C1=O&quot;, &quot;data-img&quot;: null, &quot;data-mols2grid-id-display&quot;: 23}, {&quot;mols2grid-id&quot;: 24, &quot;data-SMILES&quot;: &quot;CCOc1ccc(/C=C2\\\\C(=O)NC(=O)N(c3ccc4c(c3)OCO4)C2=O)cc1OC&quot;, &quot;data-img&quot;: null, &quot;data-mols2grid-id-display&quot;: 24}, {&quot;mols2grid-id&quot;: 25, &quot;data-SMILES&quot;: &quot;COc1cc(C)c(/C=C2\\\\C(=O)NC(=O)N(c3ccc4c(c3)OCO4)C2=O)cc1OC&quot;, &quot;data-img&quot;: null, &quot;data-mols2grid-id-display&quot;: 25}, {&quot;mols2grid-id&quot;: 26, &quot;data-SMILES&quot;: &quot;O=C(O)COc1ccc(/C=C2\\\\C(=O)NC(=O)N(c3cccc(Cl)c3)C2=O)cc1&quot;, &quot;data-img&quot;: null, &quot;data-mols2grid-id-display&quot;: 26}, {&quot;mols2grid-id&quot;: 27, &quot;data-SMILES&quot;: &quot;COc1cccc(/C=C2\\\\C(=O)NC(=O)N(c3ccccc3)C2=O)c1O[C@@H](C)C(=O)O&quot;, &quot;data-img&quot;: null, &quot;data-mols2grid-id-display&quot;: 27}, {&quot;mols2grid-id&quot;: 28, &quot;data-SMILES&quot;: &quot;O=C(O)COc1cccc(/C=C2/C(=O)NC(=O)N(c3ccccc3Cl)C2=O)c1&quot;, &quot;data-img&quot;: null, &quot;data-mols2grid-id-display&quot;: 28}, {&quot;mols2grid-id&quot;: 29, &quot;data-SMILES&quot;: &quot;COc1cccc(N2C(=O)NC(=O)/C(=C/c3ccc(OC(C)=O)c(OC)c3)C2=O)c1&quot;, &quot;data-img&quot;: null, &quot;data-mols2grid-id-display&quot;: 29}, {&quot;mols2grid-id&quot;: 30, &quot;data-SMILES&quot;: &quot;CCOc1ccc(C(=O)O)cc1/C=C1\\\\C(=O)NC(=O)N(c2ccc(OC)cc2)C1=O&quot;, &quot;data-img&quot;: null, &quot;data-mols2grid-id-display&quot;: 30}, {&quot;mols2grid-id&quot;: 31, &quot;data-SMILES&quot;: &quot;Cc1ccc(N2C(=O)NC(=O)/C(=C\\\\c3ccc(OCC(N)=O)cc3)C2=O)cc1Cl&quot;, &quot;data-img&quot;: null, &quot;data-mols2grid-id-display&quot;: 31}, {&quot;mols2grid-id&quot;: 32, &quot;data-SMILES&quot;: &quot;CCc1ccc(N2C(=O)NC(=O)/C(=C/c3ccc(OCC(=O)OC)cc3)C2=O)cc1&quot;, &quot;data-img&quot;: null, &quot;data-mols2grid-id-display&quot;: 32}, {&quot;mols2grid-id&quot;: 33, &quot;data-SMILES&quot;: &quot;O=C(O)COc1ccc(/C=C2\\\\C(=O)NC(=O)N(c3ccc(O)cc3)C2=O)cc1Cl&quot;, &quot;data-img&quot;: null, &quot;data-mols2grid-id-display&quot;: 33}, {&quot;mols2grid-id&quot;: 34, &quot;data-SMILES&quot;: &quot;Cc1ccc(Cl)cc1N1C(=O)NC(=O)/C(=C/c2cccc(OCC(N)=O)c2)C1=O&quot;, &quot;data-img&quot;: null, &quot;data-mols2grid-id-display&quot;: 34}, {&quot;mols2grid-id&quot;: 35, &quot;data-SMILES&quot;: &quot;CCOc1cc(/C=C2/C(=O)NC(=O)N(c3ccc(C(=O)OC)cc3)C2=O)ccc1O&quot;, &quot;data-img&quot;: null, &quot;data-mols2grid-id-display&quot;: 35}, {&quot;mols2grid-id&quot;: 36, &quot;data-SMILES&quot;: &quot;O=C(O)COc1ccc(Cl)cc1/C=C1\\\\C(=O)NC(=O)N(c2ccccc2)C1=O&quot;, &quot;data-img&quot;: null, &quot;data-mols2grid-id-display&quot;: 36}, {&quot;mols2grid-id&quot;: 37, &quot;data-SMILES&quot;: &quot;COc1cc(/C=C2/C(=O)NC(=O)N(c3ccccc3OC)C2=O)ccc1OC(C)=O&quot;, &quot;data-img&quot;: null, &quot;data-mols2grid-id-display&quot;: 37}, {&quot;mols2grid-id&quot;: 38, &quot;data-SMILES&quot;: &quot;CCOc1ccc(N2C(=O)NC(=O)/C(=C/c3cc4c(cc3OC)OCO4)C2=O)cc1&quot;, &quot;data-img&quot;: null, &quot;data-mols2grid-id-display&quot;: 38}, {&quot;mols2grid-id&quot;: 39, &quot;data-SMILES&quot;: &quot;COc1ccc(N2C(=O)NC(=O)/C(=C/c3ccc(OC(C)=O)c(OC)c3)C2=O)cc1&quot;, &quot;data-img&quot;: null, &quot;data-mols2grid-id-display&quot;: 39}, {&quot;mols2grid-id&quot;: 40, &quot;data-SMILES&quot;: &quot;CCOc1cc(/C=C2\\\\C(=O)NC(=O)N(c3ccc4c(c3)OCO4)C2=O)ccc1OC&quot;, &quot;data-img&quot;: null, &quot;data-mols2grid-id-display&quot;: 40}, {&quot;mols2grid-id&quot;: 41, &quot;data-SMILES&quot;: &quot;O=C(O)COc1ccc(/C=C2\\\\C(=O)NC(=O)N(c3ccccc3Cl)C2=O)cc1&quot;, &quot;data-img&quot;: null, &quot;data-mols2grid-id-display&quot;: 41}, {&quot;mols2grid-id&quot;: 42, &quot;data-SMILES&quot;: &quot;CCOc1cc2c(cc1/C=C1/C(=O)NC(=O)N(c3ccc(OC)cc3)C1=O)OCO2&quot;, &quot;data-img&quot;: null, &quot;data-mols2grid-id-display&quot;: 42}, {&quot;mols2grid-id&quot;: 43, &quot;data-SMILES&quot;: &quot;CCOC(=O)COc1ccccc1/C=C1\\\\C(=O)NC(=O)N(c2cccc(F)c2)C1=O&quot;, &quot;data-img&quot;: null, &quot;data-mols2grid-id-display&quot;: 43}, {&quot;mols2grid-id&quot;: 44, &quot;data-SMILES&quot;: &quot;CCOC(=O)COc1cc(/C=C2/C(=O)NC(=O)N(c3ccccc3)C2=O)ccc1OC&quot;, &quot;data-img&quot;: null, &quot;data-mols2grid-id-display&quot;: 44}, {&quot;mols2grid-id&quot;: 45, &quot;data-SMILES&quot;: &quot;O=C(O)COc1ccc(Cl)cc1/C=C1\\\\C(=O)NC(=O)N(c2ccc(O)cc2)C1=O&quot;, &quot;data-img&quot;: null, &quot;data-mols2grid-id-display&quot;: 45}, {&quot;mols2grid-id&quot;: 46, &quot;data-SMILES&quot;: &quot;COc1cccc(/C=C2\\\\C(=O)NC(=O)N(c3ccccc3)C2=O)c1O[C@H](C)C(=O)O&quot;, &quot;data-img&quot;: null, &quot;data-mols2grid-id-display&quot;: 46}, {&quot;mols2grid-id&quot;: 47, &quot;data-SMILES&quot;: &quot;COc1cc(/C=C2\\\\C(=O)NC(=O)N(c3ccc(C)cc3)C2=O)ccc1OCC(=O)O&quot;, &quot;data-img&quot;: null, &quot;data-mols2grid-id-display&quot;: 47}, {&quot;mols2grid-id&quot;: 48, &quot;data-SMILES&quot;: &quot;CCOc1ccc(/C=C2\\\\C(=O)NC(=O)N(c3ccc(C(=O)O)cc3)C2=O)cc1OC&quot;, &quot;data-img&quot;: null, &quot;data-mols2grid-id-display&quot;: 48}, {&quot;mols2grid-id&quot;: 49, &quot;data-SMILES&quot;: &quot;CCOc1cccc(/C=C2/C(=O)NC(=O)N(c3ccccc3)C2=O)c1OCC(=O)O&quot;, &quot;data-img&quot;: null, &quot;data-mols2grid-id-display&quot;: 49}, {&quot;mols2grid-id&quot;: 50, &quot;data-SMILES&quot;: &quot;Cc1ccc(N2C(=O)NC(=O)/C(=C\\\\c3ccccc3OCC(N)=O)C2=O)cc1Cl&quot;, &quot;data-img&quot;: null, &quot;data-mols2grid-id-display&quot;: 50}, {&quot;mols2grid-id&quot;: 51, &quot;data-SMILES&quot;: &quot;CCOc1ccc(N2C(=O)NC(=O)/C(=C/c3cccc(OCC(=O)O)c3)C2=O)cc1&quot;, &quot;data-img&quot;: null, &quot;data-mols2grid-id-display&quot;: 51}, {&quot;mols2grid-id&quot;: 52, &quot;data-SMILES&quot;: &quot;Cc1ccc(N2C(=O)NC(=O)/C(=C/c3cccc(OCC(N)=O)c3)C2=O)cc1Cl&quot;, &quot;data-img&quot;: null, &quot;data-mols2grid-id-display&quot;: 52}, {&quot;mols2grid-id&quot;: 53, &quot;data-SMILES&quot;: &quot;COc1cc(/C=C2\\\\C(=O)NC(=O)N(c3cc(C)cc(C)c3)C2=O)ccc1OCC(=O)O&quot;, &quot;data-img&quot;: null, &quot;data-mols2grid-id-display&quot;: 53}, {&quot;mols2grid-id&quot;: 54, &quot;data-SMILES&quot;: &quot;CCOC(=O)COc1ccc(/C=C2\\\\C(=O)NC(=O)N(c3ccc(F)cc3)C2=O)cc1&quot;, &quot;data-img&quot;: null, &quot;data-mols2grid-id-display&quot;: 54}, {&quot;mols2grid-id&quot;: 55, &quot;data-SMILES&quot;: &quot;CCOc1cc(/C=C2\\\\C(=O)NC(=O)N(c3ccc(C(=O)O)cc3)C2=O)ccc1OC&quot;, &quot;data-img&quot;: null, &quot;data-mols2grid-id-display&quot;: 55}, {&quot;mols2grid-id&quot;: 56, &quot;data-SMILES&quot;: &quot;CCOc1ccc(/C=C2\\\\C(=O)NC(=O)N(c3ccc(OC)cc3)C2=O)cc1C(=O)O&quot;, &quot;data-img&quot;: null, &quot;data-mols2grid-id-display&quot;: 56}, {&quot;mols2grid-id&quot;: 57, &quot;data-SMILES&quot;: &quot;COc1ccc(N2C(=O)NC(=O)/C(=C\\\\c3ccccc3O[C@@H](C)C(=O)O)C2=O)cc1&quot;, &quot;data-img&quot;: null, &quot;data-mols2grid-id-display&quot;: 57}, {&quot;mols2grid-id&quot;: 58, &quot;data-SMILES&quot;: &quot;O=C(O)COc1cccc(/C=C2/C(=O)NC(=O)N(c3cccc(Cl)c3)C2=O)c1&quot;, &quot;data-img&quot;: null, &quot;data-mols2grid-id-display&quot;: 58}, {&quot;mols2grid-id&quot;: 59, &quot;data-SMILES&quot;: &quot;CCOc1cc2c(cc1/C=C1\\\\C(=O)NC(=O)N(c3ccc4c(c3)OCO4)C1=O)OCO2&quot;, &quot;data-img&quot;: null, &quot;data-mols2grid-id-display&quot;: 59}, {&quot;mols2grid-id&quot;: 60, &quot;data-SMILES&quot;: &quot;COc1ccc(N2C(=O)NC(=O)/C(=C\\\\c3ccccc3O[C@H](C)C(=O)O)C2=O)cc1&quot;, &quot;data-img&quot;: null, &quot;data-mols2grid-id-display&quot;: 60}, {&quot;mols2grid-id&quot;: 61, &quot;data-SMILES&quot;: &quot;O=C(O)COc1ccccc1/C=C1\\\\C(=O)NC(=O)N(c2cccc(Cl)c2)C1=O&quot;, &quot;data-img&quot;: null, &quot;data-mols2grid-id-display&quot;: 61}, {&quot;mols2grid-id&quot;: 62, &quot;data-SMILES&quot;: &quot;CCc1ccc(N2C(=O)NC(=O)/C(=C/c3ccccc3OCC(=O)OC)C2=O)cc1&quot;, &quot;data-img&quot;: null, &quot;data-mols2grid-id-display&quot;: 62}, {&quot;mols2grid-id&quot;: 63, &quot;data-SMILES&quot;: &quot;O=C(O)COc1ccccc1/C=C1\\\\C(=O)NC(=O)N(c2ccc(Cl)cc2)C1=O&quot;, &quot;data-img&quot;: null, &quot;data-mols2grid-id-display&quot;: 63}, {&quot;mols2grid-id&quot;: 64, &quot;data-SMILES&quot;: &quot;CCOC(=O)COc1ccc(/C=C2\\\\C(=O)NC(=O)N(c3cccc(F)c3)C2=O)cc1&quot;, &quot;data-img&quot;: null, &quot;data-mols2grid-id-display&quot;: 64}, {&quot;mols2grid-id&quot;: 65, &quot;data-SMILES&quot;: &quot;CCOc1ccc(N2C(=O)NC(=O)/C(=C\\\\c3ccccc3OCC(=O)O)C2=O)cc1&quot;, &quot;data-img&quot;: null, &quot;data-mols2grid-id-display&quot;: 65}, {&quot;mols2grid-id&quot;: 66, &quot;data-SMILES&quot;: &quot;O=C(O)COc1ccccc1/C=C1\\\\C(=O)NC(=O)N(c2ccccc2Cl)C1=O&quot;, &quot;data-img&quot;: null, &quot;data-mols2grid-id-display&quot;: 66}, {&quot;mols2grid-id&quot;: 67, &quot;data-SMILES&quot;: &quot;NC(=O)COc1ccc(/C=C2\\\\C(=O)NC(=O)N(c3ccc(F)cc3)C2=O)cc1Cl&quot;, &quot;data-img&quot;: null, &quot;data-mols2grid-id-display&quot;: 67}, {&quot;mols2grid-id&quot;: 68, &quot;data-SMILES&quot;: &quot;C#CCOc1cc(OC)ccc1/C=C1\\\\C(=O)NC(=O)N(c2ccc(OC)cc2)C1=O&quot;, &quot;data-img&quot;: null, &quot;data-mols2grid-id-display&quot;: 68}, {&quot;mols2grid-id&quot;: 69, &quot;data-SMILES&quot;: &quot;C#CCOc1ccc(/C=C2\\\\C(=O)NC(=O)N(c3ccc([N+](=O)[O-])cc3)C2=O)cc1OC&quot;, &quot;data-img&quot;: null, &quot;data-mols2grid-id-display&quot;: 69}, {&quot;mols2grid-id&quot;: 70, &quot;data-SMILES&quot;: &quot;C#CCOc1ccc(/C=C2\\\\C(=O)NC(=O)N(c3cccc([N+](=O)[O-])c3)C2=O)cc1OC&quot;, &quot;data-img&quot;: null, &quot;data-mols2grid-id-display&quot;: 70}, {&quot;mols2grid-id&quot;: 71, &quot;data-SMILES&quot;: &quot;C#CCOc1ccc(/C=C2\\\\C(=O)NC(=O)N(c3ccc(C(=O)OC)cc3)C2=O)cc1&quot;, &quot;data-img&quot;: null, &quot;data-mols2grid-id-display&quot;: 71}, {&quot;mols2grid-id&quot;: 72, &quot;data-SMILES&quot;: &quot;C#CCOc1ccc(/C=C2\\\\C(=O)NC(=O)N(c3ccc(OC)cc3)C2=O)cc1OC&quot;, &quot;data-img&quot;: null, &quot;data-mols2grid-id-display&quot;: 72}, {&quot;mols2grid-id&quot;: 73, &quot;data-SMILES&quot;: &quot;C#CCOc1ccc(/C=C2\\\\C(=O)NC(=O)N(c3ccc(O)cc3)C2=O)cc1OCC&quot;, &quot;data-img&quot;: null, &quot;data-mols2grid-id-display&quot;: 73}]);\n",
       "\n",
       "\n",
       "// filter\n",
       "if (window.parent.mols2grid_lists === undefined) {\n",
       "    window.parent.mols2grid_lists = {};\n",
       "}\n",
       "window.parent.mols2grid_lists[&quot;default&quot;] = listObj;\n",
       "\n",
       "\n",
       "// selection\n",
       "class MolStorage extends Map {\n",
       "    multi_set(_id, _smiles) {\n",
       "        for (let i = 0; i &lt; _id.length; i++) {\n",
       "            this.set(_id[i], _smiles[i])\n",
       "        }\n",
       "    }\n",
       "    multi_del(_id) {\n",
       "        for (let i = 0; i &lt; _id.length; i++) {\n",
       "            this.delete(_id[i])\n",
       "        }\n",
       "    }\n",
       "    to_dict() {\n",
       "        var content = &#x27;{&#x27;\n",
       "        for (let [key, value] of this) {\n",
       "            content += key + &#x27;:&#x27; + JSON.stringify(value) + &#x27;,&#x27;\n",
       "        }\n",
       "        content = content.length &gt; 1 ? content.slice(0, -1) : content\n",
       "        content += &#x27;}&#x27;\n",
       "        return content\n",
       "    }\n",
       "    to_keys() {\n",
       "        var content = []\n",
       "        for (let [key] of this) {\n",
       "            content.push(key)\n",
       "        }\n",
       "        return content\n",
       "    }\n",
       "    download_smi(fileName, allItems) {\n",
       "        var content = &#x27;&#x27;\n",
       "\n",
       "        if (allItems) {\n",
       "            // Gather all smiles\n",
       "            for (var item of allItems) {\n",
       "                var smiles = item.values()[&#x27;data-SMILES&#x27;]\n",
       "                var id = item.values()[&#x27;mols2grid-id&#x27;]\n",
       "                content += smiles + &#x27; &#x27; + id + &#x27;\\n&#x27;\n",
       "            }\n",
       "        } else {\n",
       "            // Gather selected smiles\n",
       "            for (let [key, value] of this) {\n",
       "                content += value + &#x27; &#x27; + key + &#x27;\\n&#x27;\n",
       "            }\n",
       "        }\n",
       "\n",
       "        var a = document.createElement(&#x27;a&#x27;)\n",
       "        var file = new Blob([content], { type: &#x27;text/plain&#x27; })\n",
       "        a.href = URL.createObjectURL(file)\n",
       "        a.download = fileName\n",
       "        a.click()\n",
       "        a.remove()\n",
       "    }\n",
       "}\n",
       "var SELECTION = new MolStorage();\n",
       "\n",
       "\n",
       "\n",
       "// kernel\n",
       "function add_selection(grid_id, _id, smiles) {\n",
       "    SELECTION.multi_set(_id, smiles);\n",
       "    let model = window.parent[&quot;_MOLS2GRID_&quot; + grid_id];\n",
       "    if (model) {\n",
       "        model.set(&quot;selection&quot;, SELECTION.to_dict());\n",
       "        model.save_changes();\n",
       "    }\n",
       "}\n",
       "function del_selection(grid_id, _id) {\n",
       "    SELECTION.multi_del(_id);\n",
       "    let model = window.parent[&quot;_MOLS2GRID_&quot; + grid_id];\n",
       "    if (model) {\n",
       "        model.set(&quot;selection&quot;, SELECTION.to_dict());\n",
       "        model.save_changes();\n",
       "    }\n",
       "}\n",
       "if (window.parent.IPython !== undefined) {\n",
       "    // Jupyter notebook\n",
       "    var kernel_env = &quot;jupyter&quot;;\n",
       "} else if (window.parent.google !== undefined) {\n",
       "    // Google colab\n",
       "    var kernel_env = &quot;colab&quot;;\n",
       "} else {\n",
       "    var kernel_env = null;\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "// sort\n",
       "var sortField = &#x27;mols2grid-id&#x27;\n",
       "var sortOrder = &#x27;asc&#x27;\n",
       "\n",
       "// Sort dropdown\n",
       "$(&#x27;#mols2grid .m2g-sort select&#x27;).change(sort)\n",
       "\n",
       "// Sort order\n",
       "$(&#x27;#mols2grid .m2g-order&#x27;).click(flipSort)\n",
       "\n",
       "function sort(e) {\n",
       "    if (e) {\n",
       "        sortField = e.target.value\n",
       "        var selectedOption = e.target.options[e.target.selectedIndex]\n",
       "        var sortFieldDisplay = selectedOption.text\n",
       "    }\n",
       "\n",
       "    // Sort\n",
       "    if (sortField == &#x27;checkbox&#x27;) {\n",
       "        listObj.sort(&#x27;mols2grid-id&#x27;, {order: sortOrder, sortFunction: checkboxSort})\n",
       "    } else {\n",
       "        listObj.sort(sortField, {order: sortOrder, sortFunction: mols2gridSortFunction})\n",
       "    }\n",
       "\n",
       "    // Update UI.\n",
       "    $(this).parent().find(&#x27;.m2g-display&#x27;).text(sortFieldDisplay)\n",
       "}\n",
       "\n",
       "// prettier-ignore\n",
       "function flipSort() {\n",
       "    $(this).parent().removeClass(&#x27;m2d-arrow-&#x27; + sortOrder)\n",
       "    sortOrder = sortOrder === &#x27;desc&#x27; ? &#x27;asc&#x27; : &#x27;desc&#x27;\n",
       "    $(this).parent().addClass(&#x27;m2d-arrow-&#x27; + sortOrder)\n",
       "    sort()\n",
       "}\n",
       "\n",
       "function mols2gridSortFunction(itemA, itemB, options) {\n",
       "    var x = itemA.values()[options.valueName]\n",
       "    var y = itemB.values()[options.valueName]\n",
       "    if (typeof x === &#x27;number&#x27;) {\n",
       "        if (isFinite(x - y)) {\n",
       "            return x - y\n",
       "        } else {\n",
       "            return isFinite(x) ? -1 : 1\n",
       "        }\n",
       "    } else {\n",
       "        x = x ? x.toLowerCase() : x\n",
       "        y = y ? y.toLowerCase() : y\n",
       "        return x &lt; y ? -1 : x &gt; y ? 1 : 0\n",
       "    }\n",
       "}\n",
       "function checkboxSort(itemA, itemB, options) {\n",
       "    if (itemA.elm !== undefined) {\n",
       "        var checkedA = itemA.elm.querySelector(&#x27;input[type=checkbox]&#x27;).checked\n",
       "        if (itemB.elm !== undefined) {\n",
       "            var checkedB = itemB.elm.querySelector(&#x27;input[type=checkbox]&#x27;).checked\n",
       "            if (checkedA &amp;&amp; !checkedB) {\n",
       "                return -1\n",
       "            } else if (!checkedA &amp;&amp; checkedB) {\n",
       "                return 1\n",
       "            } else {\n",
       "                return 0\n",
       "            }\n",
       "        } else {\n",
       "            return -1\n",
       "        }\n",
       "    } else if (itemB.elm !== undefined) {\n",
       "        return 1\n",
       "    } else {\n",
       "        return 0\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "// grid interactions (select, click, tooltip, key events)\n",
       "// Check if selection UI is supported.\n",
       "var supportSelection = eval(&#x27;True&#x27;.toLowerCase());\n",
       "\n",
       "listObj.on(&quot;updated&quot;, initInteraction);\n",
       "\n",
       "// (Re)initialiuze all grid interaction every time the grid changes.\n",
       "function initInteraction(list) {\n",
       "    initCellClick()\n",
       "    initToolTip()\n",
       "    initKeyboard()\n",
       "    if (supportSelection) initCheckbox()\n",
       "\n",
       "\n",
       "    // Hide pagination if there is only one page.\n",
       "    if (listObj.matchingItems.length &lt;= listObj.page) {\n",
       "        $(&#x27;#mols2grid .m2g-pagination&#x27;).hide()\n",
       "    } else {\n",
       "        $(&#x27;#mols2grid .m2g-pagination&#x27;).show()\n",
       "    }\n",
       "\n",
       "    // Add a bunch of phantom cells.\n",
       "    // These are used as filler to make sure that\n",
       "    // no grid cells need to be resized when there&#x27;s\n",
       "    // not enough results to fill the row.\n",
       "    $(&#x27;#mols2grid .m2g-list&#x27;).append(&#x27;&lt;div class=&quot;m2g-cell m2g-phantom&quot;&gt;&lt;/div&gt;&#x27;.repeat(11));\n",
       "}\n",
       "\n",
       "// Cell click handler.\n",
       "function initCellClick() {\n",
       "    $(&#x27;#mols2grid .m2g-cell&#x27;).off(&#x27;click&#x27;).click(function(e) {\n",
       "        if ($(e.target).hasClass(&#x27;m2g-info&#x27;) || $(e.target).is(&#x27;:checkbox&#x27;)) {\n",
       "            // Info button / Checkbox --&gt; do nothing.\n",
       "        } else if ($(e.target).is(&#x27;div&#x27;) &amp;&amp; $(e.target).hasClass(&#x27;data&#x27;)) {\n",
       "            // Data string --&gt; copy text.\n",
       "            copyOnClick(e.target)\n",
       "        } else if ($(e.target).hasClass(&#x27;m2g-callback&#x27;)) {\n",
       "            // Callback button.\n",
       "            onCallbackButtonClick(e.target)\n",
       "        } else {\n",
       "            // Outside checkbox --&gt; toggle the checkbox.\n",
       "            if (supportSelection) {\n",
       "                var chkbox = $(this).find(&#x27;input:checkbox&#x27;)[0]\n",
       "                chkbox.checked = !chkbox.checked\n",
       "                $(chkbox).trigger(&#x27;change&#x27;)\n",
       "            }\n",
       "        }\n",
       "    })\n",
       "}\n",
       "\n",
       "// Store an element&#x27;s text content in the clipboard.\n",
       "function copyOnClick(target) {\n",
       "    var text = $(target).text()\n",
       "    navigator.clipboard.writeText(text)\n",
       "\n",
       "    // Blink the cell to indicate that the text was copied.\n",
       "    $(target).addClass(&#x27;m2g-copy-blink&#x27;)\n",
       "    setTimeout(function() {\n",
       "        $(target).removeClass(&#x27;m2g-copy-blink&#x27;)\n",
       "    }, 450)\n",
       "}\n",
       "\n",
       "// Keyboard actions.\n",
       "function initKeyboard() {\n",
       "    // Disable scroll when pressing UP/DOWN arrows\n",
       "    $(&#x27;#mols2grid .m2g-cell&#x27;).off(&#x27;keydown&#x27;).keydown(function(e) {\n",
       "        if (e.which == 38 || e.which == 40) {\n",
       "            e.preventDefault()\n",
       "        }\n",
       "    })\n",
       "\n",
       "    $(&#x27;#mols2grid .m2g-cell&#x27;).off(&#x27;keyup&#x27;).keyup(function(e) {\n",
       "        var chkbox = $(this).find(&#x27;input:checkbox&#x27;)[0]\n",
       "        if (e.which == 13) {\n",
       "            // ENTER: toggle\n",
       "            chkbox.checked = !chkbox.checked\n",
       "            $(chkbox).trigger(&#x27;change&#x27;)\n",
       "        } else if (e.which == 27 || e.which == 8) {\n",
       "            // ESC/BACKSPACE: unselect\n",
       "            chkbox.checked = false\n",
       "            $(chkbox).trigger(&#x27;change&#x27;)\n",
       "        } else if (e.which == 37) {\n",
       "            // LEFT\n",
       "            $(this).prev().focus()\n",
       "        } else if (e.which == 39) {\n",
       "            // RIGHT\n",
       "            $(this).next().focus()\n",
       "        } else if (e.which == 38 || e.which == 40) {\n",
       "            var containerWidth = $(this).parent().outerWidth()\n",
       "            var cellWidth = $(this).outerWidth() + parseInt($(this).css(&#x27;marginLeft&#x27;)) * 2\n",
       "            var columns = Math.round(containerWidth / cellWidth)\n",
       "            var index = $(this).index()\n",
       "            if (e.which == 38) {\n",
       "                // UP\n",
       "                var indexAbove = Math.max(index - columns, 0)\n",
       "                $(this).parent().children().eq(indexAbove).focus()\n",
       "            } else if (e.which == 40) {\n",
       "                // DOWN    \n",
       "                var total = $(this).parent().children().length\n",
       "                var indexBelow = Math.min(index + columns, total)\n",
       "                $(this).parent().children().eq(indexBelow).focus()\n",
       "            }\n",
       "        }\n",
       "    })\n",
       "}\n",
       "\n",
       "// Show tooltip when hovering the info icon.\n",
       "function initToolTip() {\n",
       "    $(&#x27;#mols2grid .m2g-info&#x27;).off(&#x27;mouseenter&#x27;).off(&#x27;mouseleave&#x27;).off(&#x27;click&#x27;).mouseenter(function() {\n",
       "        // Show on enter\n",
       "        $(this).closest(&#x27;.m2g-cell&#x27;).find(&#x27;.m2g-tooltip[data-toggle=&quot;popover&quot;]&#x27;).popover(&#x27;show&#x27;)\n",
       "        $(&#x27;body &gt; .popover&#x27;).click(function(e) {\n",
       "            if ($(e.target).hasClass(&#x27;copy-me&#x27;)) {\n",
       "                copyOnClick(e.target)\n",
       "            } else if ($(e.target).is(&#x27;button&#x27;)) {\n",
       "                \n",
       "            }\n",
       "        })\n",
       "    }).mouseleave(function() {\n",
       "        // Hide on leave, unless sticky.\n",
       "        if (!$(this).closest(&#x27;.m2g-cell&#x27;).hasClass(&#x27;m2g-keep-tooltip&#x27;)) {\n",
       "            $(this).closest(&#x27;.m2g-cell&#x27;).find(&#x27;.m2g-tooltip[data-toggle=&quot;popover&quot;]&#x27;).popover(&#x27;hide&#x27;)\n",
       "        }\n",
       "    }).click(function() {\n",
       "        // Toggle sticky on click.\n",
       "        $(this).closest(&#x27;.m2g-cell&#x27;).toggleClass(&#x27;m2g-keep-tooltip&#x27;)\n",
       "\n",
       "        // Hide tooltip when sticky was turned off.\n",
       "        if ($(this).closest(&#x27;.m2g-cell&#x27;).hasClass(&#x27;m2g-keep-tooltip&#x27;)) {\n",
       "            $(this).closest(&#x27;.m2g-cell&#x27;).find(&#x27;.m2g-tooltip[data-toggle=&quot;popover&quot;]&#x27;).popover(&#x27;show&#x27;)\n",
       "        } else if (!$(this).closest(&#x27;.m2g-cell&#x27;).hasClass(&#x27;m2g-keep-tooltip&#x27;)) {\n",
       "            $(this).closest(&#x27;.m2g-cell&#x27;).find(&#x27;.m2g-tooltip[data-toggle=&quot;popover&quot;]&#x27;).popover(&#x27;hide&#x27;)\n",
       "        }\n",
       "    })\n",
       "}\n",
       "\n",
       "// Update selection on checkbox click.\n",
       "function initCheckbox() {\n",
       "    $(&quot;input:checkbox&quot;).off(&#x27;change&#x27;).change(function() {\n",
       "        var _id = parseInt($(this).closest(&quot;.m2g-cell&quot;).attr(&quot;data-mols2grid-id&quot;));\n",
       "        if (this.checked) {\n",
       "            var _smiles = $($(this).closest(&quot;.m2g-cell&quot;).children(&quot;.data-SMILES&quot;)[0]).text();\n",
       "            add_selection(&quot;default&quot;, [_id], [_smiles]);\n",
       "        } else {\n",
       "            del_selection(&quot;default&quot;, [_id]);\n",
       "        }\n",
       "    });\n",
       "}\n",
       "\n",
       "// Callback button\n",
       "function onCallbackButtonClick(target) {\n",
       "    var data = {}\n",
       "    data[&quot;mols2grid-id&quot;] = parseInt($(target).closest(&quot;.m2g-cell&quot;)\n",
       "                                            .attr(&quot;data-mols2grid-id&quot;));\n",
       "    data[&quot;img&quot;] = $(target).parent().siblings(&quot;.data-img&quot;).eq(0).get(0).innerHTML;\n",
       "    $(target).parent().siblings(&quot;.data&quot;).not(&quot;.data-img&quot;).each(function() {\n",
       "        let name = this.className.split(&quot; &quot;)\n",
       "            .filter(cls =&gt; cls.startsWith(&quot;data-&quot;))[0]\n",
       "            .substring(5);\n",
       "        data[name] = this.innerHTML;\n",
       "    });\n",
       "\n",
       "    \n",
       "    // Call custom js callback.\n",
       "    None\n",
       "    \n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "/**\n",
       " * Actions\n",
       " */\n",
       "\n",
       "// Listen to action dropdown.\n",
       "$(&#x27;#mols2grid .m2g-actions select&#x27;).change(function(e) {\n",
       "    var val = e.target.value\n",
       "    switch(val) {\n",
       "        case &#x27;select-all&#x27;:\n",
       "            selectAll()\n",
       "            break\n",
       "        case &#x27;select-matching&#x27;:\n",
       "            selectMatching()\n",
       "            break\n",
       "        case &#x27;unselect-all&#x27;:\n",
       "            unselectAll()\n",
       "            break\n",
       "        case &#x27;invert&#x27;:\n",
       "            invertSelection()\n",
       "            break\n",
       "        case &#x27;copy&#x27;:\n",
       "            copy()\n",
       "            break\n",
       "        case &#x27;save-smiles&#x27;:\n",
       "            saveSmiles()\n",
       "            break\n",
       "        case &#x27;save-csv&#x27;:\n",
       "            saveCSV()\n",
       "            break\n",
       "    }\n",
       "    $(this).val(&#x27;&#x27;) // Reset dropdown\n",
       "})\n",
       "\n",
       "// Check all.\n",
       "function selectAll(e) {\n",
       "    var _id = [];\n",
       "    var _smiles = [];\n",
       "    listObj.items.forEach(function (item) {\n",
       "        if (item.elm) {\n",
       "            item.elm.getElementsByTagName(&quot;input&quot;)[0].checked = true;\n",
       "        } else {\n",
       "            item.show()\n",
       "            item.elm.getElementsByTagName(&quot;input&quot;)[0].checked = true;\n",
       "            item.hide()\n",
       "        }\n",
       "        _id.push(item.values()[&quot;mols2grid-id&quot;]);\n",
       "        _smiles.push(item.values()[&quot;data-SMILES&quot;]);\n",
       "    });\n",
       "    add_selection(&quot;default&quot;, _id, _smiles);\n",
       "};\n",
       "\n",
       "\n",
       "// Check matching.\n",
       "function selectMatching(e) {\n",
       "    var _id = [];\n",
       "    var _smiles = [];\n",
       "    listObj.matchingItems.forEach(function (item) {\n",
       "        if (item.elm) {\n",
       "            item.elm.getElementsByTagName(&quot;input&quot;)[0].checked = true;\n",
       "        } else {\n",
       "            item.show()\n",
       "            item.elm.getElementsByTagName(&quot;input&quot;)[0].checked = true;\n",
       "            item.hide()\n",
       "        }\n",
       "        _id.push(item.values()[&quot;mols2grid-id&quot;]);\n",
       "        _smiles.push(item.values()[&quot;data-SMILES&quot;]);\n",
       "    });\n",
       "    add_selection(&quot;default&quot;, _id, _smiles);\n",
       "};\n",
       "\n",
       "// Uncheck all.\n",
       "function unselectAll(e) {\n",
       "    var _id = [];\n",
       "    listObj.items.forEach(function (item) {\n",
       "        if (item.elm) {\n",
       "            item.elm.getElementsByTagName(&quot;input&quot;)[0].checked = false;\n",
       "        } else {\n",
       "            item.show()\n",
       "            item.elm.getElementsByTagName(&quot;input&quot;)[0].checked = false;\n",
       "            item.hide()\n",
       "        }\n",
       "        _id.push(item.values()[&quot;mols2grid-id&quot;]);\n",
       "    });\n",
       "    del_selection(&quot;default&quot;, _id);\n",
       "};\n",
       "\n",
       "// Invert selection.\n",
       "function invertSelection(e) {\n",
       "    var _id_add = [];\n",
       "    var _id_del = [];\n",
       "    var _smiles = [];\n",
       "    listObj.items.forEach(function (item) {\n",
       "        if (item.elm) {\n",
       "            var chkbox = item.elm.getElementsByTagName(&quot;input&quot;)[0]\n",
       "            chkbox.checked = !chkbox.checked;\n",
       "        } else {\n",
       "            item.show()\n",
       "            var chkbox = item.elm.getElementsByTagName(&quot;input&quot;)[0]\n",
       "            chkbox.checked = !chkbox.checked;\n",
       "            item.hide()\n",
       "        }\n",
       "        if (chkbox.checked) {\n",
       "            _id_add.push(item.values()[&quot;mols2grid-id&quot;]);\n",
       "            _smiles.push(item.values()[&quot;data-SMILES&quot;]);\n",
       "        } else {\n",
       "            _id_del.push(item.values()[&quot;mols2grid-id&quot;]);\n",
       "        }\n",
       "    });\n",
       "    del_selection(&quot;default&quot;, _id_del);\n",
       "    add_selection(&quot;default&quot;, _id_add, _smiles);\n",
       "};\n",
       "\n",
       "// Copy to clipboard.\n",
       "function copy(e) {\n",
       "    // navigator.clipboard.writeText(SELECTION.to_dict());\n",
       "    content = _renderCSV(&#x27;\\t&#x27;)\n",
       "    navigator.clipboard.writeText(content)\n",
       "};\n",
       "\n",
       "// Export smiles.\n",
       "function saveSmiles(e) {\n",
       "    var fileName = &quot;selection.smi&quot;\n",
       "    if (SELECTION.size) {\n",
       "        // Download selected smiles\n",
       "        SELECTION.download_smi(fileName);\n",
       "    } else {\n",
       "        // Download all smiles\n",
       "        SELECTION.download_smi(fileName, listObj.items);\n",
       "    }\n",
       "};\n",
       "\n",
       "// Export CSV.\n",
       "function saveCSV(e) {\n",
       "    content = _renderCSV(&#x27;;&#x27;)\n",
       "    var a = document.createElement(&quot;a&quot;);\n",
       "    var file = new Blob([content], {type: &quot;text/csv&quot;});\n",
       "    a.href = URL.createObjectURL(file);\n",
       "    a.download = &quot;selection.csv&quot;;\n",
       "    a.click();\n",
       "    a.remove();\n",
       "};\n",
       "\n",
       "// Render CSV for export of clipboard.\n",
       "function _renderCSV(sep) {\n",
       "    // Same order as subset + tooltip\n",
       "    var columns = Array.from(listObj.items[0].elm.querySelectorAll(&quot;div.data&quot;))\n",
       "        .map(elm =&gt; elm.classList[1])\n",
       "        .filter(name =&gt; name !== &quot;data-img&quot;);\n",
       "    // Remove &#x27;data-&#x27; and img\n",
       "    var header = columns.map(name =&gt; name.slice(5));\n",
       "    // CSV content\n",
       "    header = [&quot;index&quot;].concat(header).join(sep);\n",
       "    var content = header + &quot;\\n&quot;;\n",
       "    listObj.items.forEach(function (item) {\n",
       "        let data = item.values();\n",
       "        let index = data[&quot;mols2grid-id&quot;];\n",
       "        if (SELECTION.has(index) || SELECTION.size === 0) {\n",
       "            content += index;\n",
       "            columns.forEach((key) =&gt; {\n",
       "                content += sep + data[key];\n",
       "            })\n",
       "            content += &quot;\\n&quot;;\n",
       "        }\n",
       "    });\n",
       "    return content\n",
       "}\n",
       "\n",
       "\n",
       "// generate images for the currently displayed molecules\n",
       "var draw_opts = {&quot;n_cols&quot;: 3, &quot;width&quot;: 320, &quot;height&quot;: 240};\n",
       "var json_draw_opts = JSON.stringify(draw_opts);\n",
       "\n",
       "var smarts_matches = {};\n",
       "\n",
       "// Load RDKit\n",
       "window\n",
       ".initRDKitModule()\n",
       ".then(function(RDKit) {\n",
       "    console.log(&#x27;RDKit version: &#x27;, RDKit.version());\n",
       "    window.RDKit = RDKit;\n",
       "    window.RDKitModule = RDKit;\n",
       "\n",
       "    // Searchbar\n",
       "    function SmartsSearch(query, columns) {\n",
       "    var smiles_col = columns[0];\n",
       "    smarts_matches = {};\n",
       "    var query = $(&#x27;#mols2grid .m2g-searchbar&#x27;).val();\n",
       "    var qmol = RDKit.get_qmol(query);\n",
       "    if (qmol.is_valid()) {\n",
       "        listObj.items.forEach(function (item) {\n",
       "            var smiles = item.values()[smiles_col]\n",
       "            var mol = RDKit.get_mol(smiles, &#x27;{&quot;removeHs&quot;: false }&#x27;);\n",
       "            if (mol.is_valid()) {\n",
       "                var results = mol.get_substruct_matches(qmol);\n",
       "                if (results === &quot;\\{\\}&quot;) {\n",
       "                    item.found = false;\n",
       "                } else {\n",
       "                    item.found = true;\n",
       "                    \n",
       "                    results = JSON.parse(results);\n",
       "                    \n",
       "                    var highlights = {&quot;atoms&quot;: [], &quot;bonds&quot;: []};\n",
       "                    results.forEach(function (match) {\n",
       "                        highlights[&quot;atoms&quot;].push(...match.atoms)\n",
       "                        highlights[&quot;bonds&quot;].push(...match.bonds)\n",
       "                    });\n",
       "                    \n",
       "                    var index = item.values()[&quot;mols2grid-id&quot;];\n",
       "                    smarts_matches[index] = highlights;\n",
       "                    \n",
       "                }\n",
       "            } else {\n",
       "                item.found = false;\n",
       "            }\n",
       "            mol.delete();\n",
       "        });\n",
       "    }\n",
       "    qmol.delete();\n",
       "}\n",
       "var search_type = &quot;Text&quot;;\n",
       "// Temporary fix for regex characters being escaped by list.js\n",
       "// This extends String.replace to ignore the regex pattern used by list.js and returns\n",
       "// the string unmodified. Other calls should not be affected, unless they use the exact\n",
       "// same pattern and replacement value.\n",
       "// TODO: remove once the issue is fixed in list.js and released\n",
       "String.prototype.replace = (function(_super) {\n",
       "    return function() {\n",
       "        if (\n",
       "            (arguments[0].toString() === &#x27;/[-[\\\\]{}()*+?.,\\\\\\\\^$|#]/g&#x27;)\n",
       "            &amp;&amp; (arguments[1] === &#x27;\\\\$&amp;&#x27;)\n",
       "        ) {\n",
       "            if (this.length === 0) {\n",
       "                return &#x27;&#x27;\n",
       "            }\n",
       "            return this\n",
       "        }\n",
       "        return _super.apply(this, arguments);\n",
       "    };         \n",
       "})(String.prototype.replace);\n",
       "\n",
       "// Switch search type (Text or SMARTS)\n",
       "$(&#x27;#mols2grid .m2g-search-options .m2g-option&#x27;).click(function() {\n",
       "    search_type = $(this).text();\n",
       "    $(&#x27;#mols2grid .m2g-search-options .m2g-option.sel&#x27;).removeClass(&quot;sel&quot;);\n",
       "    $(this).addClass(&quot;sel&quot;);\n",
       "});\n",
       "\n",
       "// Searchbar update event handler\n",
       "$(&#x27;#mols2grid .m2g-searchbar&#x27;).on(&quot;keyup&quot;, function(e) {\n",
       "    var query = e.target.value;\n",
       "    if (search_type === &quot;Text&quot;) {\n",
       "        smarts_matches = {};\n",
       "        listObj.search(query, [&#x27;data-mols2grid-id&#x27;]);\n",
       "    } else {\n",
       "        listObj.search(query, [&quot;data-SMILES&quot;], SmartsSearch);\n",
       "    }\n",
       "});\n",
       "\n",
       "    \n",
       "    // Generate images for the currently displayed molecules.\n",
       "RDKit.prefer_coordgen(true);\n",
       "function draw_mol(smiles, index, template_mol) {\n",
       "    var mol = RDKit.get_mol(smiles, &#x27;{&quot;removeHs&quot;: false }&#x27;);\n",
       "    var svg = &quot;&quot;;\n",
       "    if (mol.is_valid()) {\n",
       "        var highlights = smarts_matches[index];\n",
       "        if (highlights) {\n",
       "            var details = Object.assign({}, draw_opts, highlights);\n",
       "            details = JSON.stringify(details);\n",
       "            mol.generate_aligned_coords(template_mol, true);\n",
       "        } else {\n",
       "            var details = json_draw_opts;\n",
       "        }\n",
       "        svg = mol.get_svg_with_highlights(details);\n",
       "    }\n",
       "    mol.delete();\n",
       "    if (svg == &quot;&quot;) {\n",
       "        return &#x27;&lt;svg width=&quot;320&quot; height=&quot;240&quot; xmlns=&quot;http://www.w3.org/2000/svg&quot; version=&quot;1.1&quot; viewBox=&quot;0 0 320 240&quot;&gt;&lt;/svg&gt;&#x27;;\n",
       "    }\n",
       "    return svg;\n",
       "}\n",
       "\n",
       "// Update images when the list is updated.\n",
       "listObj.on(&quot;updated&quot;, function (list) {\n",
       "    var query = $(&#x27;#mols2grid .m2g-searchbar&#x27;).val();\n",
       "    var template_mol;\n",
       "    if (query === &quot;&quot;) {\n",
       "        smarts_matches = {};\n",
       "        template_mol = null;\n",
       "    } else {\n",
       "        template_mol = RDKit.get_qmol(query);\n",
       "        template_mol.set_new_coords(true);\n",
       "    }\n",
       "    $(&#x27;#mols2grid .m2g-cell&#x27;).each(function() {\n",
       "        var $t = $(this);\n",
       "        var smiles = $t.children(&quot;.data-SMILES&quot;).first().text();\n",
       "        var index = parseInt(this.getAttribute(&quot;data-mols2grid-id&quot;));\n",
       "        var svg = draw_mol(smiles, index, template_mol);\n",
       "        $t.children(&quot;.data-img&quot;).html(svg);\n",
       "    });\n",
       "    if (template_mol) {\n",
       "        template_mol.delete();\n",
       "    }\n",
       "});\n",
       "    \n",
       "\n",
       "    // Trigger update to activate tooltips, draw images, setup callbacks...\n",
       "    listObj.update();\n",
       "    \n",
       "    // Set iframe height to fit content.\n",
       "    fitIframe(window.frameElement);\n",
       "});\n",
       "        &lt;/script&gt;\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "    &lt;/body&gt;\n",
       "&lt;/html&gt;\n",
       "\">\n",
       "</iframe>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cluster_id = 1\n",
    "cols = [\"SMILES\",\"Name\",\"Cluster\"]\n",
    "display_df = opt_cluster_df[cols].query(\"Cluster == @cluster_id\")\n",
    "mols2grid.display(display_df,subset=[\"img\"],n_cols=3,size=(320,240))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f29cabfc",
   "metadata": {},
   "source": [
    "Write the clusters to a csv file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "226e7b0f",
   "metadata": {},
   "outputs": [],
   "source": [
    "opt_cluster_df[[\"SMILES\",\"Name\",\"Cluster\"]].to_csv(\"clusters.csv\",index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1898e699",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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